# SBT Instruments — Full content for LLMs

Source: https://sbtinstruments.com/llms-full.txt
License: All content © SBT Instruments. Quotation with attribution permitted.

## SBT Instruments — homepage positioning

Source: https://sbtinstruments.com/

Growth data you can trust. Microbial process development made predictable and transparent.

One number, two unknowns. Optical density cannot tell more cells apart from bigger cells. It adds them together, along with the optics of your medium and any debris in the light path. Two runs can sit on the same OD600 curve with entirely different cultures underneath. That is why OD600 cannot compare one cultivation with the next.

Two numbers, two answers. BactoBox® counts your cells and measures their size, from the same reading, in about two minutes. How many, and what state they are in, as two separate signals. Both are direct measurements, so you can compare one run with the next.

Built for upstream microbial process development. Bacterial cultivation and fermentation, batch and fed-batch, at development and pilot scale. Typical uses are media screening and process optimisation, transfer and harvest timing, and resolving growth dynamics across process conditions.

## About SBT Instruments

Source: https://sbtinstruments.com/about

We believe growth measurements in fermentation are flawed, and we aim to change that.

SBT Instruments is a Danish company in Herlev, near Copenhagen, founded in 2014. We develop and produce BactoBox® for scientists doing upstream microbial process development. They come to us because optical density cannot tell them whether a change in the curve means more cells or bigger cells, which makes one cultivation hard to compare with the next. BactoBox® measures both, directly, from the same reading.

We care deeply about data. We hold our positions because of data, and we will change them for data. Our core technology was patented in 2017, the first BactoBox® was sold in 2020, and there are approximately 200 units in use worldwide.

## BactoBox® — direct bacterial cell count and cell size

Source: https://sbtinstruments.com/product

BactoBox® is a benchtop impedance flow cytometer. From one reading, in about two minutes, it reports how many bacterial cells are in the sample, in cells/mL, and how big those cells are. Both are headline outputs. There is no operator-dependent counting step, and the measurement is not sensitive to medium turbidity, antifoam or pigments.

Why both numbers matter: optical density cannot tell more cells apart from bigger cells, because it adds them together along with the optical properties of the medium and any debris in the light path. Two cultivations can sit on identical OD600 curves with materially different cultures behind them. This is why OD600 cannot be used to compare cultivations against each other, and why BactoBox® reports count and size separately from the same reading.

How it works: each cell passes through a microfluidic channel where an impedance signal is recorded, and the pulse features are used to distinguish cells from debris. The count is a count of whole bacterial cells per mL. The size output is the average spherical-equivalent diameter of the counted cells (CIZE), so it encodes a volume: a CIZE of 2 µm corresponds to roughly eight times the volume of a CIZE of 1 µm.

Specifications: linear quantification range 3 × 10^4 to 5 × 10^6 cells/mL, reached by diluting into range. Lower detection limit 30,000 cells/mL. Cell size detection range approximately 0.5 to 5 µm as a guideline rather than a hard cutoff. About two minutes per sample. Benchtop, 23 × 16 × 6 cm. Outputs cell concentration and cell size, with visualisation, growth curves and data export included.

Precision and agreement with plate counts: the average coefficient of variation on the cell count is 6.6 % across six bacterial species, ranging from 3.2 % to 9.6 % per species, against 15.3 % for plate counts of the same samples. 37 of 42 paired sampling points fall within 0.1 log10 of the plate count, which is 88 %, for cultures in exponential and early stationary phase. Figures are from Jordal et al. 2025 (https://doi.org/10.1016/j.mimet.2025.107284), a peer-reviewed performance qualification, and apply to the default BacTotal_v2024-10 gating configuration.

Where it fits: upstream microbial process development, bacterial cultivation and fermentation, batch and fed-batch, at development and pilot scale, wherever growth behaviour drives a decision. Typical uses are media screening and comparison, transfer and harvest timing, growth-rate comparison between runs, and end-of-fermentation crosscheck alongside CFU.

What BactoBox® does not do: it is not a live count, a viability measure, or a live-dead readout. It does not measure culturability and it does not replace CFU, which measures something different. It is not a compendial or release method, and not a substitute for a mandated QC procedure. It cannot distinguish between strains, because the count is additive across whatever is growing. Clumps, chains and doublets register as one event, so aggregating samples need a deaggregation step, and aggregates also inflate the size reading. It is a benchtop instrument with manual sampling, not an online or at-line sensor.

## Nobody measures growth accurately today

Source: https://sbtinstruments.com/knowledge/nobody-measures-growth-accurately

The evidence that optical density misleads bacterial process development.

Almost every lab in microbial upstream process development tracks growth on optical density, and it has been this way for decades. It is not a reliable way to track growth. This article is the evidence, on real cultivation data. For what the optical density number contains and why it behaves this way, see Understanding OD600.

The findings, in short. One E. coli strain grew in four media and was tracked with both OD600 and BactoBox direct cell counts. Four process interpretations made on OD600 were then checked against the real cell counts, and every one of them came out wrong. Inducing at a fixed OD600 would not have started expression from the same number of cells. A 4-hour reading severely misjudged how far each culture had come. The capacity ranking named the wrong best medium and the wrong worst. And the OD600 plateau was not a reliable marker of where growth stopped. The figures and tables below hold the numbers.

### How optical density gets every decision wrong

Process understanding is built on questions asked about different process parameters through optical density. Induce at OD600 = 0.6, compare resulting titers. Compare the growth at 4 hours. Pick the highest-capacity medium from the optical density. Conclude the process when the growth curve plateaus. Every one of these treats the optical density curve as if it were the culture itself.

To investigate this assumption, one E. coli strain was grown in four media, all inoculated at the same time and density from one starter culture, then tracked in parallel with OD600 and direct cell counts through BactoBox (see Understanding BactoBox cell counts).

Figure 1. One E. coli strain (ATCC 8739, shake flask) in four media: terrific broth, Luria-Bertani broth (LB), tryptic soy broth (TSB), and a chemically defined (CD) medium with glycerol as the carbon source. First 30 hours. Solid lines with markers are direct cell counts (left axis); dashed lines are OD600 (right axis), dilution-adjusted. The two signals disagree on the order of the media and on the distances between them. Curves start at each medium's first sample with a readable optical density. From SBT internal data; counts measured with BactoBox.

Table 1 holds the four conclusions side by side. Each question then has its own section below, with the numbers behind it.

| The question | Answered on OD600 | Answered on direct counts |
|---|---|---|
| For induction, is OD600 = 0.6 always the same culture? | By definition, yes | No. 6.9e7 to 3.5e8 cells/mL, a fivefold spread in actual cells |
| How far has each culture come at 4 h? | Terrific broth ahead of LB; TSB just behind LB; CD far behind | Terrific broth and LB level; LB holds 8x TSB's cells; CD ahead of TSB |
| Which medium has the highest capacity? | CD first, LB last | Terrific broth first, TSB last |
| When to conclude the process? | At the OD plateau | Hours away in three of four media, in both directions (Table 3) |

Table 1. What a lab would conclude on each signal. The OD600 answers mislead on all four questions.

### For induction, is OD600 = 0.6 always the same culture?

When inducing protein expression at one fixed protocol point of OD600 = 0.6, the resulting cell concentration is anything between 6.9e7 and 3.5e8 cells/mL. Cell size, measured on the same samples, explains it. At OD600 = 0.6, the TSB cells averaged a Cell-Impedance-derived siZe Estimate (CIZE) of 1.9 µm against 1.4 to 1.7 µm in the other media, and a larger cell scatters more light, so fewer cells are needed to reach the same optical density. Induce every condition at the same OD600, and each one starts producing from a different number of cells. The titers measured afterwards are then not comparable, and the screen does not find the optimal process.

Figure 2. The cell concentration behind the same OD600 = 0.6 reading, per medium. The induction point holds a fivefold spread in cells. The explanation is found in the differing cell size. Cell size (CIZE) at the induction point: TSB 1.9 µm, terrific broth 1.7 µm, LB 1.5 µm, CD 1.4 µm.

### How far has each culture come at 4 hours?

At 4 hours, terrific broth read OD600 3.4 against LB's 2.2, while the two were level on cells, 2.8e9 and 2.6e9. LB and TSB read 2.2 and 1.8 while LB held 8x as many cells, and CD read half of TSB's optical density while holding almost twice the cells. On the optical density column, a condition growing far behind raises no flag, and a condition growing well gets no credit.

Figure 3. The two readings at 4 hours: OD600 (gray bars, left axis) and direct cell counts (coloured bars, right axis), both linear from zero, interpolated between the bracketing samples in Figure 1. The gray bars step down gently while the cell bars split into two levels. Terrific broth and LB are level on cells, and TSB, mid-pack on OD600, holds the fewest.

### Which medium has the highest capacity?

Measured with OD600, CD produces the most culture and LB the least. Read on the cells, terrific broth produced the most, 3.7e10 cells/mL at the end of the run, and TSB the fewest. Both ends of the answer flip, so an optical density screen carries the wrong medium forward and drops the wrong one. The full case is worked through in Screen growth media.

| Medium | OD600 at end of run | Cells/mL at end of run |
|---|---|---|
| Terrific broth | 13.3 (2) | 3.7e10 (1) |
| LB | 4.9 (4) | 1.3e10 (3) |
| TSB | 11.1 (3) | 1.0e10 (4) |
| CD | 19.6 (1) | 3.2e10 (2) |

Table 2. The two readings at the end of the run, the last sample within the 30-hour window in Figure 1. Parentheses rank the four conditions within each column.

### When to conclude the process?

A common rule concludes a process when the optical density curve plateaus, for instance when the rise between two consecutive samples falls under 10%. Table 3 applies that rule to the dataset on OD600 and on cells/mL. In terrific broth, optical density flattens at 7.4 hours, and the real cell count reaches its plateau 4.4 hours later with 18% more cells. In CD the miss reverses. The cell count plateaus at 12.2 hours while optical density still climbs 56%, so the rule runs the process 1.9 hours longer than the cells support. The optical density plateau and the growth plateau are different events, and nothing in the optical density curve says how far apart they are, or in which direction.

| Medium | OD600 plateau | Cell-count plateau | Difference |
|---|---|---|---|
| Terrific broth | 7.4 h | 11.8 h | OD concludes 4.4 h early; 18% more cells at the count plateau |
| LB | 7.5 h | 7.5 h | Same sample |
| TSB | 12.0 h | 13.9 h | OD concludes 1.9 h early; the count is the same at both points |
| CD | 14.1 h | 12.2 h | OD concludes 1.9 h late; the count had already plateaued |

Table 3. The plateau rule, a rise under 10% between consecutive samples, applied to OD600 and to cells/mL on the curves in Figure 1. Trigger times are read at the sampled points, so their resolution is limited by the sampling intervals.

Every optical density reading behind these four answers is clean and reproducible, and nothing in them warns you that the conclusions are off.

### Closing perspective

The two places where optical density earns its keep are real. Within one unchanged cultivation it tells you whether the run behaves like the last one, and in a first plate-format screen it sorts out the worst performers fast.

You can develop a bioprocess on optical density, and the field has done so for decades. But the cost comes in two parts and should not be underestimated. In development, inaccurate growth data makes the work harder and more frustrating. Conclusions need more runs, and some of them are wrong without looking wrong. And whatever leaves development is run in production again and again. A suboptimal process produces less product, or spends more equipment time, on every run. Optical density is easy to run and close to free per sample. The cost of what it does not measure compounds for as long as the process lives.

Nothing here is specific to media. Change any process condition, temperature, pH, feed, and the cells change with it, so optical density shifts in a direction the reading cannot reveal, and the clear view of the process goes with it. This page shows one experiment. There is more data behind the same claim, including on fed-batch processes, and we are happy to walk through it if you reach out.

## Understanding OD600

Source: https://sbtinstruments.com/knowledge/understanding-od600

In most labs, OD600 is the reading used to determine harvest timing, the induction point, growth rates, which medium moves forward, when to start the feed, and whether the 100 L run behaved like the 5 L run. Every one of those decisions is read off how the culture grows over time. This article covers what OD600 reports, where it holds up, and where it misleads.

### What is OD600

Optical density at 600 nanometres (OD600) is the most common method for tracking bacterial cell concentration in liquid culture. A spectrophotometer passes light through a cuvette of culture broth and measures the fraction that reaches the detector. The number reported is called absorbance, but in a turbid bacterial suspension the signal is dominated by light scattering, not absorption. [1][2] More cells, larger cells, and more particles in the path all increase the reading.

Figure: OD600 reports the fraction of a 600 nm beam that reaches the detector. Most of the signal in a turbid bacterial culture comes from light scattering, not absorption.

The wavelength of 600 nm is a practical choice rather than a fundamental one. It sits in a window where most bacterial cultures scatter light strongly while pigments and common medium components absorb relatively little. [1] That tradeoff between sensitivity and chemical specificity is why 600 nm became the de facto standard, even though wavelengths from 540 nm to 660 nm are sometimes used.

OD600 is fast, cheap, and ubiquitous. Every cultivation lab has the equipment, every protocol quotes it, and every microbiologist knows the readout. What it gives you is a turbidity-based proxy for biomass — not a direct count of cells.

### Applications of OD600

OD600 has two dominant uses in cultivation work.

- Growth curves. Repeated readings through a culture, taken every few minutes to every few hours depending on the organism, are used to build a growth curve. The curve is then read for the lag, exponential, stationary, and decline phases of bacterial growth.

- Culture standardisation. OD600 is used to dose bacteria into downstream experiments at a target density, ensuring that experimental replicates start with comparable cell concentrations. The same logic applies when seeding fermenters at a defined inoculum.

The reason OD600 is so widely used is operational: a measurement takes under a minute and costs nothing per sample beyond the cuvette. The downside is what the number actually represents. OD600 also suits a first pass across many candidates, such as a 96-well plate where the job is to sort out the worst offenders.

### Considerations and limitations

OD600 is a biomass-related signal, not a cell count, and the gap between the two has practical consequences.

OD depends on cell size and morphology. Two cultures with identical cell numbers but different cell sizes will give different OD values. Stress, nutrient limitation, and approaching stationary phase all shift cell size, so OD can keep changing while the cell count is steady. [3][4]

Figure: Two cultures with identical cell counts can produce very different OD600 readings if cell size or morphology differs. This is one of the most consequential silent errors when comparing media, strains, or process conditions.

OD600 adds cell number and cell size together, and nothing in the reading lets you separate them afterwards. A measurement that reports the two separately, from one reading, gives you both. Impedance flow cytometry works this way. Measuring bacterial cell size covers what the size signal carries and how to read it.

OD does not distinguish cells from other particles. Insoluble medium components, antifoam droplets, precipitates after pH or temperature shifts, and cell debris from lysis all add to the reading. [5] OD cannot tell them apart from cells. Some media defeat optical density entirely, which we cover in OD600-incompatible matrices.

OD does not distinguish cells by physiological state. Microbiology recognises a continuum of states between active growth and full lysis: cells that are dividing, cells that have stopped dividing but remain metabolically active, viable but non-culturable cells, cells with damaged membranes, and lysed debris. [6] OD600 measures turbidity, and any cell, particle or fragment contributes to that signal regardless of which state it is in. After lysis, the released debris continues to contribute to some extent until it settles or is degraded.

OD600 reports a relative number. Without a calibration curve specific to the organism and the instrument — and revalidated when the medium or process conditions change — OD600 is not convertible to cells/mL or any standard biological unit. [4][7] We walk through the practicalities in building an OD600-to-CFU calibration curve.

OD600 has a narrow linear range. Above an absorbance of roughly half a unit in a standard 1 cm cuvette, the relationship between signal and concentration becomes non-linear, and dilution is required to stay in range. [3]

OD readings are instrument-dependent. Spectrophotometer geometry, detector design, and bandpass differ between models. Two instruments will give different OD values for the same sample unless they are cross-calibrated. [7]

### OD600 cannot compare cultivations against each other

OD600 works as a signal within one unchanged cultivation. It does not survive a comparison between cultivations.

Even after blanking the medium, OD600 still depends on cell size and intracellular composition. Both properties are shaped by the medium, the strain, and the cultivation conditions a cell grows under. A medium that produces cells with denser cytoplasm or more storage compounds reads higher than a medium producing the same cell count with leaner cells. Two strains with different cell sizes will give different OD curves at the same true cell concentration. Blanking corrects for the optics of the broth itself. It does not correct for what the broth has done to the cells. Screen growth media works through a media comparison where the OD ranking and the cell-count ranking disagree.

There is no single calibration factor for OD600 that survives a change in medium or strain, because the OD-to-cell ratio depends on cell properties that the new condition has changed.

Most importantly, the error is silent. The OD curve looks clean, the ranking is internally consistent, the results of a single run are reproducible, and the ranking can still be wrong, with no diagnostic from inside OD that tells you when.

### Alternatives

Several methods coexist with OD600 in cultivation labs. Each answers a slightly different question.

The methods that count cells directly have existed for a long time. They have stayed off the growth curve because they are too slow or too laborious to run as a time series, so OD600 has kept the job by default.

| Method | What it measures | Strengths | Limitations |
|---|---|---|---|
| CFU plating | Cells competent to form colonies on a defined medium | Direct, biologically meaningful, regulatory standard for QC release | Slow (24–72 h or longer), labour-intensive, misses viable but non-culturable cells, undercounts aggregates |
| Dry cell weight | Total dried biomass per volume | Calibrated mass unit, used for yield calculations | Slow (hours), destructive, insensitive at low concentrations, biomass is not cell number |
| Fluorescence flow cytometry | Per-cell scatter and fluorescence; can include viability and metabolism dyes | Rich per-cell information | Expensive; complex sample prep; primarily designed for eukaryotes; undercounts aggregates |
| BactoBox (impedance flow cytometry) | Every whole bacterial cell in the sample, counted one cell at a time and reported as cells/mL, together with the average size of those cells, from the same reading. See Understanding BactoBox cell counts and Measuring bacterial cell size | Cell number and cell size reported separately from one reading, ~2 minutes per sample, benchtop format | Electrical signal only — no versatile tags or dyes; undercounts aggregates and reads them as larger cells without proper sample preparation |

The fast method is indirect, and the direct ones have been slow. That is the trade the field has lived with.

### Common behaviour of OD600

Common questions and answers.

Why does my OD600 keep rising after my cells have stopped dividing?

Cells continue to change size and refractive properties for a while after division has slowed or stopped. Depending on the organism and the limitation that triggered slowdown, cells may shrink, swell with storage granules such as polyhydroxyalkanoates or glycogen, or change shape. [8][9] Each of those changes alters how the cell scatters light at 600 nm even though no new cells have been added. [1][3] The result is an OD600 that drifts upward — or downward — even though the cell count has plateaued. The OD plateau and the moment cell division actually ends rarely line up exactly, which is the subject of identifying the harvest point.

Can OD600 tell me whether my cells are alive or dead?

No, but the more accurate question is what definition of "alive" the experiment requires, because microbiology recognises several distinct definitions and OD600 cannot answer any of them. [6] Cells can be actively dividing, non-dividing but metabolically active, viable but non-culturable, structurally intact but metabolically inactive, or partially lysed. Each of these states still contributes to turbidity to varying degrees, because turbidity reflects the bulk presence of light-scattering material in the broth, not any individual cell's state. A flat or slowly declining OD tail can therefore hide a culture that has lost culturability without losing structure, a population of whole cells that is dropping, or a culture beginning to lyse. Asking whether cells are alive requires deciding which definition matters for the decision at hand — culturable, metabolically active, structurally intact, or membrane-intact — and choosing a method that targets that specific definition.

Why does my OD600 jump after I add antifoam, inducer, or a base correction?

Many additions introduce particulates, precipitates, or refractive-index shifts that change the OD signal independently of the cell population. Antifoams form droplets that scatter light. pH corrections can precipitate medium components. Inducer stocks sometimes carry insolubles. The OD jump is real, but it is not a change in the cells.

Why is my OD600 different on a different spectrophotometer?

OD600 is not a standardised quantity. Instruments differ in optical path geometry, detector design, and spectral bandpass, so the same sample can read differently between machines. A 2020 inter-laboratory study across 244 laboratories found that calibration against serial dilutions of silica microspheres allows OD-derived cell counts to be compared across instruments, but without such calibration, OD readings cannot be meaningfully compared between instruments. [7]

### Closing

OD600 is fast and convenient, and within one unchanged cultivation it will tell you whether a run is behaving like the last one. It will not tell you whether one cultivation grew better than another. The reading adds cell number and cell size together, and both of them change when the medium or the conditions change, so two runs can sit on the same OD curve with different cultures behind them.

Measured separately, and often enough to follow the culture through the run, count and size give a growth curve you can compare with another one.

### References

1. Myers JA, Curtis BS, Curtis WR. Improving accuracy of cell and chromophore concentration measurements using optical density. BMC Biophys. 2013;6:4. https://doi.org/10.1186/2046-1682-6-4

2. Koch AL. Turbidity measurements of bacterial cultures in some available commercial instruments. Anal Biochem. 1970;38(1):252-9. https://doi.org/10.1016/0003-2697(70)90174-0

3. Stevenson K, McVey AF, Clark IBN, Swain PS, Pilizota T. General calibration of microbial growth in microplate readers. Sci Rep. 2016;6:38828. https://doi.org/10.1038/srep38828

4. Mira P, Yeh P, Hall BG. Estimating microbial population data from optical density. PLoS One. 2022;17(10):e0276040. https://doi.org/10.1371/journal.pone.0276040

5. Routledge SJ. Beyond de-foaming: the effects of antifoams on bioprocess productivity. Comput Struct Biotechnol J. 2012;3:e201210014. https://doi.org/10.5936/csbj.201210014

6. Davey HM. Life, death, and in-between: meanings and methods in microbiology. Appl Environ Microbiol. 2011;77(16):5571-6. https://doi.org/10.1128/AEM.00744-11

7. Beal J, Farny NG, Haddock-Angelli T, Rai V, Davies J, Patron N, et al. Robust estimation of bacterial cell count from optical density. Commun Biol. 2020;3:512. https://doi.org/10.1038/s42003-020-01127-5

8. Åkerlund T, Nordström K, Bernander R. Analysis of cell size and DNA content in exponentially growing and stationary-phase batch cultures of Escherichia coli. J Bacteriol. 1995;177(23):6791-7. https://doi.org/10.1128/jb.177.23.6791-6797.1995

9. Slaninova E, Sedlacek P, Mravec F, Mullerova L, Samek O, Krzyzanek V, et al. Light scattering on PHA granules protects bacterial cells against the harmful effects of UV radiation. Appl Microbiol Biotechnol. 2018;102(4):1923-31. https://doi.org/10.1007/s00253-018-8760-8

## Understanding BactoBox® cell counts

Source: https://sbtinstruments.com/knowledge/understanding-bactobox-cell-counts

You cannot develop a bacterial process without measuring how the culture grows. Harvest timing, induction points, media selection, feed strategy, scale-up comparisons. Every one of them is read off growth over time.

Growth is not tracked accurately today. OD600 is one number that blends cell count with cell size, morphology, the optical properties of the medium, and any debris in the light path. The consequence is that OD600 cannot be used to reliably compare one cultivation with another. Change the medium or the conditions and the cells change with them, so the OD-to-cell relationship moves underneath you.

Growth is tracked accurately when cell count and cell size are measured separately, which is what BactoBox® enables. The count is the number you follow, because growth is cell division. Size is what makes the count trustworthy.

This article covers what BactoBox® reports, how the measurement is made, and how it compares with the methods already in the lab.

### What BactoBox® measures

BactoBox® counts every whole bacterial cell in a sample, one cell at a time, and reports cells/mL and cell size. A cell that has stopped dividing is counted. A cell that no longer forms a colony is counted. A cell that takes up propidium iodide is counted. Lysis is what removes a cell from the count, because a cell that has come apart is no longer there.

A whole cell is a simple thing to define. It is either there as a closed object or it is not. What grows into a colony on a chosen medium, and what takes up a dye under chosen staining conditions, both depend on choices made in the assay, and both move when those choices move. A whole-cell count does not, which is what makes it straightforward to work with and comparable from one run to the next.

It is also the number an OD600 reading is standing in for. Growth is cell division, so counting cells is the direct version of the measurement optical density approximates. The difference is that OD600 adds cell number and cell size together, along with everything else in the light path, and never says which of them moved. BactoBox® reports the two separately, from the same measurement.

### How the measurement works

BactoBox® is a benchtop instrument that counts bacterial cells one cell at a time as they pass through a microfluidic flow cell. It uses impedance flow cytometry. A pair of microelectrodes detects the way each particle perturbs an electrical field as it crosses between them,[1] and every particle within the instrument's 0.5–5 µm detection range is recorded as a single event.

Figure: The BactoBox® flow cell. Each bacterial cell passes between two microelectrodes and perturbs an electrical field as it crosses the measurement zone.

#### Cells and particles

Each event is then classified. A bacterial cell produces the characteristic impedance signature for as long as it is a closed object with its cytoplasm inside, and as long as its size falls in the bacterial range. A cell with a damaged and permeable membrane still meets both conditions, so it is still counted as a cell. This is why a cell that stains with propidium iodide is counted.

Once a cell has lysed, its membrane fragments and its contents disperse. What is left is either below the size the instrument detects, or far enough from the signature of a whole cell that it is not classified as one. Salt crystals, antifoam droplets, insoluble medium components and other debris in the same size range are not cells either.

Figure: How each event is classified. A whole bacterial cell is counted as a cell. Lysed remains and non-cell debris are not, though every detected event still counts toward total/mL.

That classification is what produces two numbers from one measurement. cells/mL is the concentration of whole bacterial cells. total/mL is the concentration of every particle detected, the cells included, and the distance between the two says how much of what is in the sample is not a cell.

Being able to tell cells from particles is also what lets a direct count work in matrices that defeat optical density. OD600 sums everything in the light path and has no way to say what is a cell and what is not, so an opaque or particulate medium makes the reading uninterpretable. BactoBox® decides one particle at a time, so the cell count survives a background that OD600 cannot see through. Our article on OD600-incompatible matrices works through where this matters.

One thing follows from counting one event at a time. A doublet, a short chain or a clump of cells crosses the measurement zone as a single event, so it registers as one cell carrying a larger signature. The count runs low and the size runs high at the same time. An accurate count of an aggregating sample therefore needs a preparation step that breaks the clumps apart. We encourage users to deaggregate, and we offer an analytical service to develop tailored protocols.

#### Cell size

BactoBox® reports size as CIZE, the average spherical-equivalent diameter of the counted cells. This is the diameter of a sphere holding the same volume as the cell, so the number encodes a volume, and a small change in the number is a large change in size. A population at CIZE 2 µm holds roughly eight times the volume of one at CIZE 1 µm.

Size is what makes the count trustworthy. Separating cell number from cell size is the one thing an optical density reading cannot do, so reporting both is the evidence that the count is clean. Size also carries a signal of its own, and it often moves before the count does. Coming out of lag phase the count can sit flat near the seeding level while size climbs steeply, because cells build the machinery to divide before they divide. Our article on measuring bacterial cell size covers how to read it.

#### The default setting

These properties do not come from the hardware alone. They follow from the way SBT defines the measurement, and that definition is what BactoBox® runs on its default setting. We ship it on every instrument and we recommend it, and it is what your instrument is running unless SBT has specifically set up something else with you.

### How BactoBox® compares to the methods already in the lab

One framing that recurs through this section is worth flagging in advance. None of the methods below measure live cells. "Live" is not a property that can be tested on an individual bacterial cell without changing it, and the operational definitions in use are population-level stand-ins.[2] What these methods measure are specific, well-defined properties. Whether a cell is still whole, whether a dye can cross its membrane, whether it forms a colony on a chosen medium, and how much light a suspension scatters. These are different properties, and the differences matter.

#### BactoBox® and OD600

A common misconception is that BactoBox® is a different route to the same kind of signal OD600 produces. OD600 is the optical density at 600 nm read on a spectrophotometer, and it is the workhorse signal in most cultivation labs. It is not a cell count. It is a turbidity-based proxy for biomass that responds to cell number, but also to cell size, morphology, and any non-cell particulates in the light path. Two runs can sit on identical OD curves with materially different cultures behind them, and nothing in the OD data reveals it.

There are two places where OD600 remains the right tool. Within one cultivation, with the same strain, medium, vessel and instrument, it works as a fingerprint for whether a run is behaving like the last one. In early high-throughput screening, where the job is to sort out the worst performers in a plate format, it is fast, cheap and good enough. What it cannot support is a trustworthy comparison between cultivations, because changing the medium or the conditions changes the cells and moves the OD-to-cell relationship with them. Our article on understanding OD600 covers the signal, its uses and its limitations in depth.

#### BactoBox® and CFU

A colony-forming unit (CFU) is, operationally, a unit in the sample capable of forming a visible colony on a chosen medium under chosen conditions. CFU counts are not counts of every cell present. They are counts of cells competent to grow into a colony in that specific assay. Cells that are physically whole but have lost the ability, perhaps temporarily, to grow do not register as CFUs.[3] CFU also undercounts samples containing aggregates, because a clump of multiple cells grows up as a single colony. The arithmetic that turns colonies into a concentration assumes every cell plated produces its own countable colony, and clumping breaks that assumption.[4] CFU is sensitive to the plating workflow itself as well. Differences between pour plating and dehydrated film methods such as Petrifilm have been documented on the same samples, running in both directions depending on the organism.[5]

A BactoBox® count and a CFU count therefore measure overlapping but non-identical populations. Through exponential and early stationary phase, where most whole cells are also culturable, the two track each other closely. In work we published, a direct impedance count correlated near-perfectly with CFU across six bacterial genera.[6] The two diverge in decline phase, where cells stop forming colonies but stay counted until they lyse.

CFU measures culturability. It is not a measure of viability, though the two are routinely treated as the same thing.

#### BactoBox® and direct microscopy

Direct microscopy with a counting chamber such as a Petroff-Hausser or hemocytometer is the textbook reference for counting cells directly. Brightfield or phase-contrast counts measure visible particles in a small sample volume. Fluorescence microscopy with a total-DNA stain such as DAPI or acridine orange, usually after fixation or mild permeabilisation, measures cells whose DNA is accessible to the dye. Fluorescence microscopy with a membrane-integrity stain such as PI paired with SYTO 9 reports cells whose membranes have been compromised, on the same logic as the dye combination used in fluorescent flow cytometry.

The advantage no other method on this list provides is that a human can see the cells. Morphology, filament and aggregate structure, and sub-populations are all directly visible. This matters in particular for aggregating cultures, where microscopy resolves the structure that BactoBox® compresses into one event per particle. In practice, though, many labs do not use direct microscopy routinely. Throughput is slow, and the counted volume is a very small fraction of the sample. The count also depends on field selection, focus plane, and judgements about what counts as a cell. Petroff-Hausser counts of 1 µm microbeads, at bacterial size scale, have been measured 28% off the supplier's reported bead count,[7] within the chamber manufacturer's own stated 20–30% expected count discrepancy.

#### BactoBox® and fluorescent flow cytometry

Fluorescent flow cytometry shares its architecture with BactoBox®. Cells pass one cell at a time through a sensing region, and each one is detected as an event. The difference is what is sensed. A fluorescent instrument reads light scatter and the fluorescence of bound dyes. BactoBox® reads an electrical signal from the cell itself.

That difference decides what each one reports. The most common combination in bacterial work is propidium iodide (PI) paired with SYTO 9. PI cannot cross an undamaged bacterial membrane, so the panel sorts the population by membrane permeability.[8] The non-permeable fraction is usually reported as the intact cell count, and it is often read from there as a live cell count, even though the field has no agreed definition of a live or viable cell.[2]

Stains are also not universal, though they are often treated as though they were. Dye behaviour changes from one organism to the next. In one published comparison, SYTO 9 alone gave equal signal intensity for live and dead Staphylococcus aureus, but an eighteen-fold stronger signal for dead Pseudomonas aeruginosa than for live ones, with the same effect seen in Escherichia coli and not in the gram-positive Bacillus subtilis. The same study reports that the SYTO 9 signal bleaches strongly and decreases over time, and that maximum PI intensities are weak compared with SYTO 9 and with background.[8] A panel therefore needs assay development for the organism at hand, or at minimum careful interpretation.

BactoBox® works the other way round. No stain is added. Each object that passes the electrodes is assessed on whether its electrical properties match those of a whole bacterial cell. A cell killed by heat, oxygen or antibiotics is usually PI-positive, so it drops out of the non-permeable fraction on a flow cytometer. On its default setting, BactoBox® still counts it, because killing a cell does not necessarily break it open. That is why cells/mL tracks the total event count in a flow cytometry panel. If you are comparing BactoBox® against a fluorescence flow cytometer, compare against the total event count.

This is not a claim that a direct count is free of sample effects. Particles in the detection window still have to be told apart from cells, and aggregates still register as one event. Those show up in the data. Dye-dependent bias does not.

Figure: What each method measures. Whole-cell count, dye-defined properties, culturability, and biomass-related signal are different properties of a bacterial population. The methods routinely used in cultivation labs answer different questions and are not interchangeable.

### What direct cell counts make possible

A BactoBox® cells/mL reading is a measurement of a specific, well-defined property. It is the concentration of whole bacterial cells in the sample, and it corresponds directly to what scientists usually mean by growth, which is cell division. A direct count rises when, and only when, division actually happens.

This makes a direct cell count a must-have for growth-related measurements. A new object in the bacterial size range can only be a new cell. A specific growth rate calculated from a series of cells/mL measurements is therefore an unambiguous measurement of how fast the population is dividing. OD600 cannot say this cleanly, because increases can also come from cell elongation, morphology shifts, or storage compound accumulation. CFU is too laborious to run at the frequency that meaningful growth-rate measurements require, and its answer arrives days after the sample was taken.

Beyond growth rate, a direct count makes each phase of the growth curve more interpretable. Lag, the true exponential phase, the onset of slowdown, peak cell concentration, plateau, and lysis. Lysis shows up as a drop in cells/mL, because a cell that has come apart stops being counted.

Sequential measurements are where the value compounds. With a series of measurements through a run, the trajectory itself becomes the unit of analysis, and differences between strains, media, or runs become differences in trajectory shape rather than endpoint comparisons.

### Conclusion

A direct count of whole cells is a different kind of measurement from the ones most cultivation labs have built their workflows around. It is not a faster OD600 and it is not a faster CFU. It answers the question process development turns on, and that the other routine methods cannot answer, which is the true rate at which the culture is dividing.

BactoBox® produces both numbers in about two minutes per sample, with no stain, no plate, and no manual counting step. CFU, by comparison, takes a day or more and adds variance from manual counting. The BactoBox® result is fast enough to be repeated through a cultivation, and it does not carry the operator-to-operator spread that a manual count does.

What this enables, when integrated into a process development workflow, is a clearer view of the dynamics that drive decisions at the bench. When exponential growth begins and ends, where the cell-count peak sits, when slowdown sets in, when a plateau is reached, and when lysis begins. Working from cells/mL is, at the same time, a new way of thinking about a process for many scientists. The growth curve has been read through OD600 and CFU for a generation, and re-anchoring interpretation in direct cell counts takes time.

Talk to us about your medium, organism, and workflow.

### References

1. Bertelsen CV, Skands GE, González Díaz M, Dimaki M, Svendsen WE. Using impedance flow cytometry for rapid viability classification of heat-treated bacteria. ACS Omega. 2023;8(8):7714-21. https://pubs.acs.org/doi/10.1021/acsomega.2c07357

2. Davey HM. Life, death, and in-between: meanings and methods in microbiology. Appl Environ Microbiol. 2011;77(16):5571-6. https://journals.asm.org/doi/10.1128/aem.00744-11

3. Oliver JD. Recent findings on the viable but nonculturable state in pathogenic bacteria. FEMS Microbiol Rev. 2010;34(4):415-25. https://academic.oup.com/femsre/article/34/4/415/538375

4. Martini KM, Boddu SS, Nemenman I, Vega NM. Maximum likelihood estimators for colony-forming units. Microbiol Spectr. 2024;12(9):e03946-23. https://journals.asm.org/doi/10.1128/spectrum.03946-23

5. Linton RH, Eisel WG, Muriana PM. Comparison of conventional plating methods and Petrifilm for the recovery of microorganisms in a ground beef processing facility. J Food Prot. 1997;60(9):1084-8. https://pubmed.ncbi.nlm.nih.gov/31207841/

6. Jordal PL, Díaz MG, Aalund F, Skands G. Performance qualification of impedance flow cytometry as a rapid in-process control proxy for colony-forming units in bacterial fermentation processes. J Microbiol Methods. 2025;238:107284. https://www.sciencedirect.com/science/article/pii/S0167701225002003

7. Rahman KMT, Butzin NC. Counter-on-chip for bacterial cell quantification, growth, and live-dead estimations. Sci Rep. 2024;14(1):782. https://www.nature.com/articles/s41598-023-51014-2

8. Stiefel P, Schmidt-Emrich S, Maniura-Weber K, Ren Q. Critical aspects of using bacterial cell viability assays with the fluorophores SYTO9 and propidium iodide. BMC Microbiol. 2015;15:36. https://bmcmicrobiol.biomedcentral.com/articles/10.1186/s12866-015-0376-x

## Measuring bacterial cell size

Source: https://sbtinstruments.com/knowledge/measuring-bacterial-cell-size

What the size signal carries, and how to read it through a fermentation, batch or fed-batch

An increase in optical density is easy to read. The number went up, so there must be more cells. But that does not follow. OD600 rises with cell number, and it rises again when the same number of cells get bigger, and it cannot tell the two apart. [1]

Cell size is part of what makes OD600 ambiguous. But cell size is also a process signal in its own right. In mammalian cell culture, mean cell diameter is tracked as a routine read on how a culture is doing. Microbial process development has rarely had that lens, because following bacterial cell size has meant microscopy or flow cytometry, and neither sits easily beside a running fermenter. BactoBox measures it next to the direct cell count, from the same reading, in about two minutes.

This article covers what cell size means for a bacterium, what the signal carries, and how to read it beside the count.

### What OD600 hides

Optical density sums everything in the light path. Cell number, cell size, morphology, the optical properties of the medium, and any debris. One number comes out, and it never says which of those moved. Understanding OD600 works through the signal and its limits in full.

The practical consequence is that two runs can sit on the same OD600 curve with materially different cultures behind them.

Figure 1. The trap a bulk signal hides. Two runs sit on an identical OD600 curve. In the on-target run the direct cell count rises with OD600 and cell size holds the steady baseline a feed running to its profile is meant to produce. In the struggling run far fewer cells form, but each is larger, and the two offset so that turbidity, and therefore OD600, stays the same. A direct cell count and a cell size tell the runs apart; OD alone cannot. Schematic, for illustration.

The error is silent. The OD curve looks clean, the run is internally consistent, and nothing inside the OD data says which of the two you have.

The same ambiguity sits inside a single run. Cell size falls as a batch culture proceeds, so more and more cells are needed to reach the same OD600 reading, and one reading cannot be turned into a cell number with a single factor. It does not move in exact proportion either, because OD600 responds to cell shape and refractive index as well as size. [1] In one series of E. coli runs the number of cells behind one unit of OD600 climbed five- to sevenfold between early growth and late exponential phase. Building an OD600-to-CFU calibration curve works through that data.

### Size is best described as volume, not length

Bacteria come in many shapes, from rods to spheres to chains, and they change shape as conditions change. A length, or the longest dimension, does not compare cleanly between cells, because two cells of the same length can hold very different amounts of material.

The quantity that does carry across shapes is the total volume of the cell. That is also what impedance flow cytometry, the principle behind BactoBox, senses. As each cell passes the sensor, the size of the electrical signal scales with its volume. [2] BactoBox reports this as Cell-Impedance-derived siZe Estimate (CIZE), the average spherical-equivalent diameter of the counted cells, which is the diameter of a sphere holding the same volume as the cell. Because the number encodes a volume, a small change in CIZE is a larger change in size than it looks. Cells in a population at CIZE 2 um hold roughly eight times the volume of cells at CIZE 1 um. Understanding BactoBox cell counts covers how the count and the size are produced.

### What the size signal carries

Watching cell size is routine in mammalian cell culture. In Chinese hamster ovary (CHO) fed-batch production, a distinct cell-size-increase phase is documented [3], and shifts in mean cell size track changes in how much product the cells make. [4] Size is treated there as information about the state of the cells, not only their number.

The same is true for bacteria. Cell size is a read on the physiological state of the culture. [5] A cell size that holds still through a stretch of a run points to cells that are working the same way through it. A cell size that moves says something in them has changed, whether that is a shift in metabolism, a response to what is left in the medium, or the start of a problem. The reading does not say which of those it is.

Through a batch cultivation, cell size moves a great deal. In E. coli batch cultures in rich media, average cell size starts to decrease during exponential growth, well before the culture leaves it. [6] In our own runs, across six media, the mean cell volume falls four- to tenfold between its early peak and late exponential phase. Over the first six doublings of one of those runs the cell count is a straight line on a log axis with no detectable change in rate, while cell volume falls by a factor of two and a half. So the cells are changing while the growth curve says nothing has. Exponential growth is not the same as balanced growth. A culture can divide at a constant rate while what is inside the cells keeps changing. [5][6]

A fed-batch process is a different problem. The feed is there to hold the culture in one physiological state, so while the feed is running to its profile the expectation is a cell size that holds still. A size that moves anyway says the cells have left that state, and in the classic shift experiments that move arrives well ahead of any change in the division rate. [7] On a process that is still being tuned, that is an early warning.

Cell size moves in both directions and for several reasons. Starvation pulls the cytoplasm away from the cell wall as the cell loses water [8], cells growing slowly in poor conditions are smaller overall [5], and a block on division, such as the SOS response to DNA damage, lets a cell keep growing and elongate. [9][10]

The signal shows up early, ahead of the cell count itself. Cells are known to grow in size during lag phase, before any of them divide. [11] Figure 2 is one real E. coli batch culture, with the direct cell count and the cell size taken from the same BactoBox readings. Through the lag phase, roughly the first 1.4 hours, the cell concentration holds near the level it was seeded at, and the population is not yet expanding. Cell size climbs steeply over the same window, from about 0.9 to 2.0 um, which is roughly a tenfold increase in cell volume. Cell size then falls away through the exponential phase that follows, from about 2.0 to 1.4 um while the count rises more than three hundredfold, which is a little over a threefold drop in cell volume.

Figure 2. One E. coli batch culture, followed on BactoBox. The culture was seeded into fresh LB medium at about 1x10^6 cells/mL from a starter culture in early stationary phase, then sampled over the next four and a half hours. The direct cell count is in cells/mL and the cell size is the average spherical-equivalent diameter of the counted cells. Through the lag phase (about 1.4 hours) the concentration holds near the seeding level while cell size climbs from about 0.9 to 2.0 um; because the diameter encodes a volume, that is roughly a tenfold increase in cell volume. Through the exponential phase that follows, the count rises more than three hundredfold while cell size falls back from about 2.0 to 1.4 um, a little over a threefold drop in cell volume. From SBT internal data, 15 January 2025.

### How to read the size trace

Four habits make the size trace usable.

Interpret size and the count together. Changes in cell size add important information about your culture. A count that is flat while size climbs is usually a culture coming out of lag. A count that is flat while size is flat too is a culture that is not changing.

Read the trajectory, not the value. There is no target CIZE. What carries information is how size moves through a run, and how that compares with the same process last time.

Do not read direction as quality. Smaller is not worse and bigger is not better. Cells shrink under nutrient limitation and elongate when division is blocked [8][9], and both are departures from a culture that is growing as intended.

Establish the normal trace first. Run the process you already trust and record what size does through it. A departure only means something once you know what the process normally does.

### Why this matters in fed-batch fermentation

That makes cell size a feed-design signal. A feed that is too rich, too lean, or mistimed pushes the cells off the state the process was tuned for, and a size trace read beside the count shows the move directly rather than through a proxy. Followed through a run, it reads how the culture answers a feed change, which is information a feed-rate profile is currently tuned without. It is also where a feed that has drifted shows first.

In a fed-batch E. coli process, the textbook example is acetate overflow. When carbon comes in faster than the cells can oxidise it, they spill the excess into acetate, and the acetate holds back both growth and product formation. [12] By the same reasoning, the cells change state before the count shows it, and cell size is where that shows. Telling that apart from the size rise of a culture accelerating normally is what the normal trace above is for.

The organism, the medium and the feed settle how large the size response is, how fast it arrives, and whether it is large enough to read. That is what the first runs on a new process are for.

### Closing

For decades the growth curve has been a single quantity followed over time, and for most bacterial cultivation work that quantity is OD600. [1] Cell size adds a second one to the same curve. It gives an understanding of what state the cells are in, what they are doing, and, because size moves first, where the count is going next, from the same reading and at the same cadence.

### References

1. Stevenson K, McVey AF, Clark IBN, Swain PS, Pilizota T. (2016). General calibration of microbial growth in microplate readers. Scientific Reports, 6, 38828. https://doi.org/10.1038/srep38828
2. Sun T, Morgan H. (2010). Single-cell microfluidic impedance cytometry: a review. Microfluidics and Nanofluidics, 8(4), 423-443. https://doi.org/10.1007/s10404-010-0580-9
3. Pan X, Dalm C, Wijffels RH, Martens DE. (2017). Metabolic characterization of a CHO cell size increase phase in fed-batch cultures. Applied Microbiology and Biotechnology, 101(22), 8101-8113. https://doi.org/10.1007/s00253-017-8531-y
4. Schellenberg J, Nagraik T, Wohlenberg OJ, Ruhl S, Bahnemann J, Scheper T, Solle D. (2022). Stress-induced increase of monoclonal antibody production in CHO cells. Engineering in Life Sciences, 22(5), 427-436. https://doi.org/10.1002/elsc.202100062
5. Jun S, Si F, Pugatch R, Scott M. (2018). Fundamental principles in bacterial physiology - history, recent progress, and the future with focus on cell size control: a review. Reports on Progress in Physics, 81(5), 056601. https://doi.org/10.1088/1361-6633/aaa628
6. Akerlund T, Nordstrom K, Bernander R. (1995). Analysis of cell size and DNA content in exponentially growing and stationary-phase batch cultures of Escherichia coli. Journal of Bacteriology, 177(23), 6791-6797. https://doi.org/10.1128/jb.177.23.6791-6797.1995
7. Kjeldgaard NO, Maaloe O, Schaechter M. (1958). The transition between different physiological states during balanced growth of Salmonella typhimurium. Journal of General Microbiology, 19(3), 607-616. https://doi.org/10.1099/00221287-19-3-607
8. Shi H, Westfall CS, Kao J, et al. (2021). Starvation induces shrinkage of the bacterial cytoplasm. Proceedings of the National Academy of Sciences, 118(24), e2104686118. https://doi.org/10.1073/pnas.2104686118
9. Ultee E, Ramijan K, Dame RT, Briegel A, Claessen D. (2019). Stress-induced adaptive morphogenesis in bacteria. Advances in Microbial Physiology, 74, 97-141. https://doi.org/10.1016/bs.ampbs.2019.02.001
10. Mukherjee A, Cao C, Lutkenhaus J. (1998). Inhibition of FtsZ polymerization by SulA, an inhibitor of septation in Escherichia coli. Proceedings of the National Academy of Sciences, 95(6), 2885-2890. https://doi.org/10.1073/pnas.95.6.2885
11. Rolfe MD, Rice CJ, Lucchini S, et al. (2012). Lag phase is a distinct growth phase that prepares bacteria for exponential growth and involves transient metal accumulation. Journal of Bacteriology, 194(3), 686-701. https://doi.org/10.1128/JB.06112-11
12. Eiteman MA, Altman E. (2006). Overcoming acetate in Escherichia coli recombinant protein fermentations. Trends in Biotechnology, 24(11), 530-536. https://doi.org/10.1016/j.tibtech.2006.09.001

## Identifying the harvest point

Source: https://sbtinstruments.com/knowledge/identifying-the-harvest-point

Every batch cultivation ends with a harvest decision, and that decision determines how much product comes out of the fermenter. The processes most directly served by this article fall into two related groups.

Figure 1. The two families of batch cultivation processes this article serves.

What both target groups have in common is that the harvest decision is timed relative to a single moment in the run: the transition from exponential growth into stationary phase — when cell division stops. For cell-as-product workflows this transition is the harvest target itself. Sometimes the harvest sits a defined time after the transition, and the transition is still the anchor against which that defined time is measured. In both cases, knowing precisely when the transition occurred is what makes the harvest decision rational rather than habitual.

The interpretive principles below apply to any accurate count of whole bacterial cells, regardless of the method used to obtain it. For the foundational explanation of what a BactoBox® cell count is and how it compares to other enumeration methods, see Understanding BactoBox® cell counts. For the OD600 limitations summarised below in their fuller form, see Understanding OD600.

Figure 2. The transition from exponential growth into stationary phase — the moment cell division stops (violet marker) — is the anchor for the harvest decision. Depending on the process, the harvest sits at that moment or a defined time after it.

### Anchoring the harvest decision

For processes where the cells are themselves the product, the typical harvest target is the peak in culturable cell concentration, also known as the peak CFU count. Holding the run past this peak either accumulates cost in extended stationary or begins to lose culturable cells in the decline phase. Stopping the run before the peak leaves culturable product in the fermenter.

For some processes the optimal harvest sits a defined time after the transition rather than at it. The onset of stationary phase in Escherichia coli triggers a σS-dependent gene-expression program that activates dozens of genes encoding stress-response, transport, and metabolic functions [1]; the σS regulator is conserved in most γ-proteobacteria [2], and analogous stationary-phase regulatory programs operate in other bacterial groups under different alternative sigma factors. A product that accumulates as a consequence of this regulatory shift — a surface antigen whose expression turns on as cells stop dividing, a secondary metabolite that builds up during stationary phase — is best harvested some hours after the transition, not at it. The mechanism in these cases is the natural biology of stationary phase, not heterologous induction.

The same logic extends to fed-batch and cell-factory processes: even when the harvest decision is dominated by product titre, knowing the precise moment cells stopped dividing can help anchor decisions about feed-rate transitions and induction-strategy timing. The argument below focuses on the cell-as-product and stationary-phase-product cases, where the timing relationship to the cell-count trajectory is most direct.

### Why OD600 cannot answer the question reliably

OD600 is a turbidity measurement, not a cell count. The signal at 600 nm is dominated by light scattering, which depends on cell size, intracellular composition, and the refractive properties of the cells in their current physiological state [3,4]. In a batch cultivation none of those properties holds still. In rich media, E. coli cell volume climbs to a maximum in early exponential growth and then falls to roughly a fifth of that peak by stationary phase [5,6], and the decline sets in before the culture leaves exponential phase [5]. The number of cells behind one unit of OD600 moves with it. Within each E. coli run in our own calibration dataset that factor climbs roughly five- to sevenfold from early growth to late exponential phase, worked through in Building an OD600-to-CFU calibration curve. OD also keeps responding to those property changes after division has slowed or stopped [3]. The OD reading drifts on the basis of cell properties rather than cell number.

In practice, when an OD curve is compared with a direct cell count on the same run, three patterns are possible.

OD plateaus before the cell count does. The OD trace flattens while cells are still dividing. A harvest called on this plateau stops the run before the transition into stationary phase is actually reached.

OD continues to climb after the cell count has plateaued. The cells stop dividing, but OD keeps rising because cell size, intracellular composition, or refractive properties continue to change. A harvest called on this plateau sits in the fermenter long after the population has stopped producing, with the run either accumulating cost in extended stationary or beginning to lose culturable cells in the decline phase. A decision intended to be timed relative to the transition — for example, a harvest at three hours into stationary phase — is also pushed late, because the reference point itself has moved.

OD plateaus at the same point the cell count plateaus. Possible, and some strains and media behave this way. Across the many runs BactoBox® users have measured, this alignment is the rare exception rather than the rule.

The practical consequence is that the moment cell division actually stops is not visible from inside an OD trace alone, and any decision timed against it inherits the OD reading's drift.

### A worked example

The figure below shows an internal batch of a Pseudomonas strain, with OD600 and CFU/mL measured in triplicate at every timepoint. OD600 was read on diluted samples and adjusted back, so the trace stays inside the linear range of the instrument. The two curves are read on different axes: OD600 on the left, CFU/mL on a log axis on the right.

Figure 3. OD600 and CFU/mL on a single batch cultivation of aPseudomonas strain, sampled in triplicate at each timepoint (error bars: OD standard deviation across the three measurements, CFU geometric standard deviation factor). OD600 approaches a plateau at approximately 13.6 hours. CFU/mL continues to rise past that point, reaching its observed peak around 15.4 hours. The cell count climbs roughly 40% between the OD plateau and the actual cell-count peak.

What the data shows is straightforward. The OD curve approaches a plateau at approximately 13.6 hours; from there it stays within a few percent of its maximum value for the remainder of the run. A reasonable reading of OD alone would conclude that the culture has entered stationary phase at that point. The CFU curve, taken on the same samples, says otherwise — culturable cells continue to be added for nearly two more hours. The CFU value at 15.4 hours is roughly 40% higher than the value at the OD plateau.

A harvest called on the OD signal here ends the run before the population has finished dividing. Over a production calendar, the cumulative effect of a gap of that size can be a material fraction of recoverable product, and the operator running the OD signal will never see the gap from inside the run because the OD reading itself looks like a clean stationary plateau. This is one of the three patterns described above; on a different run, the same operator might face the opposite failure mode, with OD continuing to climb after cell division has already stopped.

### What a direct cell count makes possible

A direct count of cells does not carry the OD signal's interpretive ambiguity. The number rises only when new cells appear in the sample, and a sequence of measurements through the slowdown into early stationary phase shows directly when the addition of new cells stops. The cell-count plateau is the transition into stationary phase, observed in real time.

What that visibility supports is broader than a single harvest decision. For a process where the cells are the product, the plateau is the target. For a process whose product accumulates after cell division has stopped, the harvest sits a defined time after the plateau, and the timing of that defined time is now grounded in the biology rather than in elapsed time from inoculation. The shift is from harvesting against OD and hoping the proxy aligns, to harvesting against a known position relative to the moment cell division stopped — known in real time.

BactoBox counts bacterial cells in approximately two minutes per sample using impedance flow cytometry. Two minutes is short enough to make a sequence of measurements across the slowdown practical without dedicated analytical staff. The published equivalence between BactoBox and CFU is worth being explicit about. BactoBox cells/mL and CFU/mL were compared across six bacterial species spanning a range of envelope types and cell sizes, with log-log R² values between 0.9974 and 0.9998 through exponential growth, deceleration, and stationary phase, against a minimum of 0.9025 in the USP <1223> framework for alternative quantitative microbiological procedures [7,8]. Across those phases, 88% of head-to-head measurements differed by less than 0.1 log10 units [7]. The two methods diverge in the decline phase, where BactoBox continues to count whole cells that have lost culturability [7]. Through the regime where the harvest decision is actually anchored, BactoBox produces a result consistent with what a CFU plate count would produce a day or two later.

### Closing

The harvest decision in a cell-as-product fermentation turns on a single question: has cell division stopped. OD600 does not answer that question reliably across runs, because the OD signal can plateau before division stops, continue to climb after division stops, and only sometimes track the cell-count plateau directly. A sequence of direct cell counts through the slowdown answers the question by measuring the thing the question is about — when new cells stop appearing in the sample.

The cell-count peak is not universally the harvest target. For products that accumulate in stationary phase, the harvest sits later; for seed-culture transfers, it sits earlier. The argument of this article is that knowing precisely when the transition into stationary phase occurred is what enables those decisions to be timed accurately, whether the harvest lands at the transition or at a defined point relative to it. The resulting decision is anchored in the dynamics of the culture rather than in a proxy whose relationship to those dynamics is not fixed.

### References

1. Lacour S, Landini P. SigmaS-dependent gene expression at the onset of stationary phase in Escherichia coli: function of sigmaS-dependent genes and identification of their promoter sequences. J Bacteriol. 2004;186(21):7186-95. https://journals.asm.org/doi/10.1128/jb.186.21.7186-7195.2004

2. Bouillet S, Bauer TS, Gottesman S. RpoS and the bacterial general stress response. Microbiol Mol Biol Rev. 2024;88(1):e0015122. https://journals.asm.org/doi/10.1128/mmbr.00151-22

3. Stevenson K, McVey AF, Clark IBN, Swain PS, Pilizota T. General calibration of microbial growth in microplate readers. Sci Rep. 2016;6:38828. https://doi.org/10.1038/srep38828

4. Mira P, Yeh P, Hall BG. Estimating microbial population data from optical density. PLoS One. 2022;17(10):e0276040. https://doi.org/10.1371/journal.pone.0276040

5. Åkerlund T, Nordström K, Bernander R. Analysis of cell size and DNA content in exponentially growing and stationary-phase batch cultures of Escherichia coli. J Bacteriol. 1995;177(23):6791-97. https://doi.org/10.1128/jb.177.23.6791-6797.1995

6. Nieto C, Igler C, Singh A. Inferring bacterial cell size dynamics across media conditions. Sci Rep. 2026;16:9883. https://doi.org/10.1038/s41598-026-38811-1

7. Jordal PL, Díaz MG, Aalund F, Skands G. Performance qualification of impedance flow cytometry as a rapid in-process control proxy for colony-forming units in bacterial fermentation processes. J Microbiol Methods. 2025;238:107284. https://doi.org/10.1016/j.mimet.2025.107284

8. United States Pharmacopeia. General Chapter <1223> Validation of Alternative Microbiological Methods. USP-NF. 2021:4-6. https://doi.org/10.31003/USPNF_M9153_01_01

## Screen growth media

Source: https://sbtinstruments.com/knowledge/screen-growth-media

### The decision you make once

Most batch bioprocesses run for a long time on a single medium. The choice of medium is made early in development, and because changing it means re-validating the process, it is reviewed only periodically — typically for strains that carry a large share of production. The cost of getting it wrong is therefore not paid once. It is paid on every batch the medium runs, on every fermenter, until that review comes around.

Most of the screening work that drives this decision, across candidate media, and across the adjacent screening of process conditions, is read on OD600. Direct cell-counting methods, and CFU plating, may enter the workflow later, particularly for processes where the cells themselves are the product, or as occasional spot checks against the OD curve. The bulk of the comparison and ranking that drives the decision, however, is done on the proxy of OD600. This article is about what happens when the proxy and real cell counts disagree, and how to know which one to trust.

### The OD ranking on four candidate media

In the experiment, E. coli ATCC 8739 was grown in shake flask across four candidate media: terrific broth (TB), Luria-Bertani broth (LB), tryptic soy broth (TSB), and a chemically defined medium, Bacto CD Supreme FPM (Thermo Fisher Scientific), with 10 mL/L glycerol as the carbon source. The first three are complex media; CD is chemically defined. For each medium, OD600 was tracked through a growth curve over 30 hours and the maximum value was recorded. This is a representative version of the OD-based screen most process development teams use to compare candidate media in early development.

Figure: Figure 1. Peak OD600 across the four candidate media.

The OD ranking, best to worst, is CD, TB, TSB, LB. CD reads approximately 61% higher than TB at peak. TSB sits above LB by a smaller margin. A scientist concluding the screen on this signal alone would commit to CD for scale-up, with TB as the runner-up and LB the candidate to drop. That is the ranking the experiment produces if the only column is OD600.

The same experiment tracked direct cell counts in parallel, using BactoBox®. For more details on how BactoBox® works, see Understanding BactoBox® cell counts . BactoBox® cell counts have been benchmarked against colony-forming-unit plating across E. coli and additional bacterial species in fermentation processes, with near-perfect correlation through exponential, deceleration, and stationary phases. [1] The next plot adds those measurements alongside the OD bars on the same flasks.

### The same screen with direct cell counts

Figure: Figure 2. Peak OD600 (grey bars, left axis) and peak direct cell count (purple bars, right axis) for the same four candidate media. The two signals rank the media differently.

The cell-count ranking, best to worst, is TB, CD, LB, TSB. TB reaches a peak of approximately 4e10 cells/mL, roughly 21% more than CD. LB produces about 18% more cells than TSB, in the opposite order to what OD suggested.

Two rank flips appear in the same screen.

The best medium flips from CD to TB. CD reads about 61% higher than TB on OD, but produces about 17% fewer cells. A scientist screening on OD alone would carry CD forward and discard or deprioritise TB. The actual highest-concentration medium for this strain in this experiment is the one OD ranked second. A 17% gap on cells is not a marginal difference. Once a process is committed, it is 17% of cell mass per batch, on every fermenter, for as long as the process runs — a permanent loss of production capacity that an OD screen alone cannot detect.

The worst medium flips from LB to TSB. LB sits lowest on OD but produces about 18% more cells than TSB. A scientist deprioritising the lowest OD reading would deprioritise the wrong candidate. TSB, which OD placed third of four, is in fact the lowest-yielding medium of the four.

Without the cell-count column, neither rank flip is visible. The OD curves are clean, the peaks are well-defined, and the ranking is reproducible. The data passes review. The answer is wrong on two of the four ranks.

The full step-by-step protocol for running this comparison on the latest version of BactoBox® is available on the SBT help center.

### Why OD and cell counts can disagree

OD600 measures the optical scattering of a culture, and the magnitude of that scattering depends on more than the number of cells in the path. It also depends on cell size, the refractive index and density of the cytoplasm, the presence of intracellular storage compounds, and the optical properties of any non-cell particulates in the medium. [2][3] Each of these is shaped by the medium itself, which means the relationship between OD and cell count is not a constant — it is a property of the cultivation conditions.

The biology behind this is well established and is not specific to E. coli . Schaechter, Maaløe and Kjeldgaard showed in 1958 that bacterial cell size and macromolecular composition vary systematically with growth medium and temperature. [4] Volkmer and Heinemann reproduced and extended this picture in E. coli , demonstrating that cell volume and total dry mass per cell change with growth rate and conditions. [5] The same dependency has been demonstrated in Bacillus subtilis , a Gram-positive species phylogenetically distant from the enterics, where median cell length scales with nutrient availability across rich and nutrient-poor media. [6] The cells in a richer medium are not just more numerous; they are also a different size and composition than the cells in a leaner medium. The OD signal cannot separate those contributions.

Across the four media, the OD signal per cell varies by roughly threefold. No single calibration factor applied across the screen would have rescued the OD ranking, and inter-laboratory work confirms that no general OD-to-cells conversion exists even within a single organism. [2][7] For a more detailed treatment of what OD600 measures and the conditions under which it can and cannot be trusted, see Understanding OD600 .

### Why the disagreement is hard to catch

The disagreement between OD ranking and cell ranking is structural rather than anomalous. There is nothing in the OD data that signals an error. The curves are clean, the ranking is internally consistent, and a repeat of the experiment will return the same ranking. Each of those properties is what good data looks like, and each of them is true at the same time as the ranking being wrong.

What makes the situation difficult to recognise from inside an OD screen is precisely the absence of any internal diagnostic. There is no feature of an OD curve that flags that the cells in one flask are larger than the cells in another, or that the cytoplasmic density differs across media. A careful experiment and a sloppy experiment converge to the same ranking when both are read on the same proxy.

### Why CFU spot checks are not a safety net

A reader who runs CFU plates as a verification step against the OD curve may reasonably object that this is not a problem in their workflow — the ranking is, after all, anchored against an actual cell count somewhere downstream. In practice, a CFU spot check helps only if it lands at the right moment. The culturable count in a batch cultivation rises, peaks, and often falls rapidly. A CFU sample taken before the peak, after the peak, or anywhere on the slope does not return the maximum the medium can deliver.

OD does not reliably tell you when that peak is. The same medium-induced drift in cell size and intracellular contents that distorts the cross-media ranking can also cause OD to decouple from culturable cell count, with the OD signal continuing to rise or remaining elevated after cell division has stalled. [2] A scientist relying on the OD curve to time a CFU sample may take it before or after the peak without knowing it. Plating frequently across the cultivation solves the timing problem but is operationally expensive and is not what occasional spot checks are intended to do.

### The cost downstream

The cost of an OD-driven media error has a particular shape worth being explicit about. A medium, once chosen, runs every batch until the next optimisation round, and that interval is often long. A 17% under-yield on cells is not a single-experiment loss; it is 17% of fermenter throughput, batch after batch, against the fixed costs of the rest of the plant — depreciation, utilities, labour, downstream processing, and quality control. And an optimisation round still read on OD only repeats the same flawed comparison.

A process development scientist does not need to make the throughput argument at the bench. But the decision made at the bench is the input that determines throughput later. A decision grounded in cell counts is therefore made in the same units that determine throughput downstream, which avoids a translation step from a proxy that does not transfer cleanly across media in the first place.

### Closing perspective

This is not an argument to abandon OD600. Within a single cultivation, where the cell properties are approximately stable, OD remains a fast and useful real-time signal to gauge if the process is running consistently. It is the comparison across cultivations on different media, or different process conditions like temperature, pH, or carbon-source level, that OD does not handle cleanly, because the cell properties OD depends on are themselves what is changing. A media ranking on OD is not a ranking on cells; it is a ranking on the product of cell number and the cell properties each medium happens to induce. Some of the time those two rankings agree, by coincidence. Often they do not.

Cells/mL at peak is also not the only input into a media decision. Growth rate, time to peak, raw-material cost, supply considerations, and — for processes whose product is a recombinant protein or a metabolite — the relationship between cell density and product titre, all matter. A complete media choice weighs them. The narrower argument made here is that when the question is which medium produces the most cells, the answer should be read on cell counts, not on OD. The same logic applies to other batch screens, including seed-train decisions; fed-batch media selection involves feed strategy and is not addressed by this analysis.

A medium chosen on cells/mL is the medium the screen was always meant to identify, in the units the decision was always meant to be made on. The expensive case — the one this article is about — is the screen where the rankings disagree and the cell-count column is missing.

### References

1. Jordal, P. L., Díaz, M. G., Aalund, F., & Skands, G. (2025). Performance qualification of impedance flow cytometry as a rapid in-process control proxy for colony-forming units in bacterial fermentation processes. Journal of Microbiological Methods, 238, 107284. https://doi.org/10.1016/j.mimet.2025.107284

2. Stevenson, K., McVey, A. F., Clark, I. B. N., Swain, P. S., & Pilizota, T. (2016). General calibration of microbial growth in microplate readers. Scientific Reports, 6, 38828. https://doi.org/10.1038/srep38828

3. Mira, P., Yeh, P., & Hall, B. G. (2022). Estimating microbial population data from optical density. PLoS ONE, 17(10), e0276040. https://doi.org/10.1371/journal.pone.0276040

4. Schaechter, M., Maaløe, O., & Kjeldgaard, N. O. (1958). Dependency on medium and temperature of cell size and chemical composition during balanced growth of Salmonella typhimurium. Journal of General Microbiology, 19(3), 592–606. https://doi.org/10.1099/00221287-19-3-592

5. Volkmer, B., & Heinemann, M. (2011). Condition-dependent cell volume and concentration of Escherichia coli to facilitate data conversion for systems biology modeling. PLoS ONE, 6(7), e23126. https://doi.org/10.1371/journal.pone.0023126

6. Weart, R. B., Lee, A. H., Chien, A. C., Haeusser, D. P., Hill, N. S., & Levin, P. A. (2007). A metabolic sensor governing cell size in bacteria. Cell, 130(2), 335–347. https://doi.org/10.1016/j.cell.2007.05.043

7. Beal, J., Farny, N. G., Haddock-Angelli, T., et al. (2020). Robust estimation of bacterial cell count from optical density. Communications Biology, 3, 512. https://doi.org/10.1038/s42003-020-01127-5

## End of fermentation crosscheck

Source: https://sbtinstruments.com/knowledge/end-of-fermentation-crosscheck

When BactoBox® is introduced as a new measurement in a bacterial process development workflow, a common starting point is to take an end-of-fermentation sample and compare the cells/mL reading from BactoBox® with a CFU count from the same sample. The comparison is intuitive, but the result is easy to misread. A direct count of whole cells and a CFU count measure different properties of the population [1][2] , and the relationship between them depends on where in the fermentation the sample was taken and how the sample was handled in the plating workflow.

This article walks through how to read an end-of-fermentation result. Each of the three possible outcomes — agreement, a higher BactoBox® count, or a lower BactoBox® count — reveals something about whether the process has untapped potential, recoverable yield, or counts that may be reading too low. The interpretive principles below apply to any direct count of whole cells, regardless of the method used to obtain it. For the foundational explanation of what a BactoBox® cell count is and how it compares to other enumeration methods, see Understanding BactoBox® cell counts . For the OD600 limitations that recur through this article, see Understanding OD600 . For the workflow that produces a reliable cells/mL versus CFU comparison from sampling through to plate readout, see our cross-check protocol on the help center .

The article is most directly relevant to processes where the cells are themselves the product — probiotics, vaccines, biocontrol agents, seed cultures for downstream cultivation — and where a culturable or total cell count is the unit of interest. The diagnostic patterns also apply to recombinant-protein and metabolite processes when the question at hand is whether the culture is still growing, though in those processes process value also depends on titre, which is outside the scope of this article.

### What the comparison should look like at end-of-fermentation

In a process designed around cell yield, the end-of-fermentation sample is typically drawn in late exponential or early stationary phase, where most whole cells are also culturable. In this window, BactoBox® and CFU track each other closely [1] . An end-of-fermentation comparison that lands in close agreement is therefore the expected outcome and a sign that the process is behaving in the expected window. When the two counts disagree, the disagreement is informative — it points either to an opportunity to recover yield, or to an issue in the analytical workflow that is hiding the true cell count.

The three patterns below describe each form of disagreement and what it suggests. In every case, OD600 is unable to act as a referee on the same sample, because OD600 responds to cell size, intracellular composition, and non-cell particulates as well as to cell number [3][4] . The same OD value is consistent with multiple states of the underlying culture, including states that imply very different conclusions about process potential.

Figure: Figure 1. Three patterns at end-of-fermentation. Agreement between BactoBox® and CFU is consistent with a sample taken in late exponential or early stationary phase. A higher BactoBox® count points to a culture that has passed its culturable peak or to incomplete colony recovery. A lower BactoBox® count points to cell aggregation. Each pattern is an entry point for understanding whether a process has more potential.

### Pattern 1 — the BactoBox® count and the CFU count agree

The cells/mL reading and the CFU count are in close agreement. This is consistent with a sample taken while most cells are still culturable, typically during exponential or early stationary phase [1] .

The measurement itself does not point to an immediate problem, but it does not rule out a subtler one — the culture may not yet have reached its cell-count peak at the time the sample was taken. Agreement between the two counts confirms that the sample falls in the window where they should agree, but not that it sits at the plateau. There may be more cell yield available by allowing the process to continue further. Confirming that the cell count has actually plateaued requires a sequence of measurements through the slowdown into early stationary phase.

### Pattern 2 — the BactoBox® count is higher than the CFU count

The sample contains more whole cells than culturable cells. There are two distinct explanations, and both translate directly into recoverable yield.

The first explanation is that the culture has continued past the point where culturability peaks. A fraction of the cells will still be whole while no longer forming colonies on the plate. The comparison does not say whether those cells are dead or have entered a viable-but-non-culturable (VBNC) state [2] , and the operational conclusion does not depend on which it is — not all cells in the sample are culturable any more. In practical terms, an earlier sampling point could yield more culturable product from the same process without changing anything else.

An assumption worth surfacing. Throughout this article we treat the stationary phase of a batch cultivation as the cessation of cell division. That is consistent with every batch cultivation we have looked at, both our own data, customer data, and the published literature we are aware of. The alternative reading — that stationary phase can be a steady state in which cell division continues at a rate balanced by cell death — is sometimes asserted, but to our knowledge it is not supported by direct evidence. If you have data showing active cell division during the stationary phase of a batch cultivation, we would be very interested in seeing it.

The second explanation is that the chosen plating technique is not recovering all culturable cells. For the same organism, pour plating, spread plating, and drop plating expose cells to different physical and thermal conditions during plating, and can give different counts on the same sample as a result. Pour plating, for example, briefly mixes cells with molten agar at approximately 45 to 50 °C, and that exposure can reduce recovery for heat-sensitive or already-stressed cells, while spread and drop plating avoid the molten-agar step entirely [5] . A useful internal check is to plate a single sample by more than one technique and compare. If the techniques disagree on a sample where they should agree, the plating workflow is contributing to the discrepancy with the direct cell count, and the gap may close once the workflow is reviewed.

The OD600 reading does not separate these two explanations from each other or from a correctly-timed sample. The discrepancy between cells/mL and CFU is doing the diagnostic work.

### Pattern 3 — the BactoBox® count is lower than the CFU count

The cells/mL reading appears lower than the CFU count. At the single-cell level this is not physically possible, so the explanation lies in how the sample is presented to each method.

When cells in the sample have formed aggregates, a cluster of cells passes through the BactoBox® flow cell — the microfluidic channel where each particle is detected as a single event — as one event rather than as the several cells it contains. Larger aggregates can fall outside the detectable particle size range entirely and contribute nothing to the BactoBox® count, while still producing a single colony on a plate. On a plate, the same cluster may give rise to one colony, or the aggregate may break apart during plating and produce several [6] . In every variation, the CFU count registers more events than BactoBox® on the same sample, and a BactoBox® count lower than CFU is therefore a strong indication that the sample contains aggregates.

The downstream point worth making is that if the end product is reported in CFU, there is a meaningful risk that the CFU count is also undercounting the population, because aggregated cells that grow up as a single colony are recorded as one [6] . The cell count the process is actually producing may be higher than the CFU number suggests. Investigating whether a disaggregation step would change the result is often worthwhile. If a disaggregation step is introduced, it should be applied consistently to the samples measured by both methods for the comparison to remain meaningful.

### Conclusion

A single end-of-fermentation comparison, read carefully, can already point to where yield is being left on the table or where the reported count may be misleading. Agreement between BactoBox® and CFU confirms the sample sits in a sensible window, but does not confirm it sits at the cell-count peak — there may be more cell yield available by allowing the process to continue. A BactoBox® count higher than CFU points to a culture that has passed its culturable peak or to incomplete colony recovery on the plate, both of which translate into recoverable yield. A BactoBox® count lower than CFU points to aggregation, and the cell count the process is actually producing may be higher than the CFU number suggests.

In each case, the crosscheck is an entry point. A fuller picture comes from following the cell count through the slowdown and early stationary phase.

For help interpreting a specific result on a process you are working with, reach out. Other articles in our help center cover related applications of cells/mL in fermentation development.

### References

1. Jordal PL, Díaz MG, Aalund F, Skands G. Performance qualification of impedance flow cytometry as a rapid in-process control proxy for colony-forming units in bacterial fermentation processes. J Microbiol Methods. 2025;238:107284. https://www.sciencedirect.com/science/article/pii/S0167701225002003

2. Oliver JD. Recent findings on the viable but nonculturable state in pathogenic bacteria. FEMS Microbiol Rev. 2010;34(4):415–425. https://academic.oup.com/femsre/article/34/4/415/538375

3. Stevenson K, McVey AF, Clark IBN, Swain PS, Pilizota T. General calibration of microbial growth in microplate readers. Sci Rep. 2016;6:38828. https://doi.org/10.1038/srep38828

4. Mira P, Yeh P, Hall BG. Estimating microbial population data from optical density. PLoS One. 2022;17(10):e0276040. https://doi.org/10.1371/journal.pone.0276040

5. Sanders ER. Aseptic laboratory techniques: plating methods. J Vis Exp. 2012;(63):e3064. https://doi.org/10.3791/3064

6. Martini KM, Boddu SS, Nemenman I, Vega NM. Maximum likelihood estimators for colony-forming units. Microbiol Spectr. 2024;12(9):e03946-23. https://journals.asm.org/doi/10.1128/spectrum.03946-23

## Building an OD600-to-CFU calibration curve

Source: https://sbtinstruments.com/knowledge/od600-to-cfu-calibration

### Turning OD600 into a cell number

This article walks through the steps of building an OD600-to-CFU calibration curve, and the pitfalls that come with it. It then introduces a method that avoids those pitfalls, by measuring the cells directly.

Most cultivation labs read growth through OD600, because it is fast and runs at the bench. What many of them actually want is a cell number, often the colony-forming units (CFU) they would get from a plate count. The usual bridge between the two is a calibration curve that converts an OD600 reading into a CFU-equivalent. The curve is easy to build once and convenient to reuse. The difficulty is that the relationship it captures is not stable, so a curve built under one set of conditions quietly stops being correct under another. This article covers how the OD600-to-CFU calibration is normally built, why OD600 and CFU stop tracking each other, and how a direct cell count changes the picture.

### How to build an OD to CFU calibration curve

The procedure itself is straightforward. The care is in the sampling.

- Grow the strain in the exact medium and conditions you intend to use the curve for. The curve will only be valid for those conditions, so match them from the start.

- Through the run, pull paired samples across the whole density range you care about, several points per order of magnitude, from early growth into stationary phase.

- For each sample, read OD600 and plate for CFU. Dilute the sample so the OD reading falls in the spectrophotometer's linear range, then multiply back. Plate in replicate so each CFU point is an average of several plates rather than a single one [2] . Make sure to cool the CFU samples when pulled, since fast-growing organisms can rapidly skew your calibration results.

- Plot CFU/mL against OD600 and fit the relationship over the density window you will actually work in, rather than forcing one line across the whole curve. The fit holds only for that window.

- Record the strain, medium, and conditions next to the curve. That label is part of the result.

Done carefully, this gives a usable conversion for that strain, in that medium, over that range. The trouble starts when any of those conditions change.

### Where the calibration slips

OD600 is turbidity, not a count. The signal responds to cell number, but also to cell size, cell shape [3] , and any non-cell particulates in the light path [1] . The number of cells behind one unit of OD600 is therefore not a fixed conversion factor.

The conversion factor moves across a single growth curve. In the E. coli runs in Figure 1, the number of cells behind one OD600 unit climbs roughly five- to sevenfold from early growth to late exponential phase, the upward slope of every curve. The main driver is cell size, which does not hold constant through exponential growth and often begins to fall well before a culture leaves that phase [4]. Smaller cells scatter less light, so more of them are needed to reach the same OD600 (cell size is not plotted in Figure 1, but it was measured alongside every count in these same runs, and it falls across the range sampled here). A single conversion factor cannot be right at both ends of the same run. Reused across the whole curve, the way a standard-curve slope usually is, it is off by several-fold, overstating the count early and understating it late, which flattens the very growth curve you are trying to read.

The calibration factor also moves between conditions, which is the larger problem. Figure 1 follows the same E. coli strain grown in four different media and plots the conversion factor itself, the number of cells behind one unit of OD600, against the OD600 reading. The curves sit at different heights, so at a typical calibration density, around OD600 = 1, the factor differs about fivefold between media. Because a direct cell count agrees closely with the plate count through exponential and early stationary phase (R² above 0.99 across six species [5] ), this cells-per-OD factor is, for practical purposes, the OD600-to-CFU factor a calibration is built to capture. A curve built in one medium misreads another by roughly that margin, and a change in strain, aeration, or temperature shifts it again. The growth rate is not spared either. In these cultures the exponential rate read from OD600 came out 20 to 32 percent below the rate from direct counts, by a medium-dependent margin.

Figure: Figure 1. The y-axis is the OD600-to-cell conversion factor: the number of cells/mL that sit behind one unit of OD600 (OD600 = 1) — in other words, the number you would multiply an OD600 reading by to get a cell count. The x-axis is the OD600 reading itself. If that factor were constant, a single number would convert OD600 to cells anywhere in a run, and every line here would be flat. It never is. One E. coli strain was grown in four media and followed with direct cell counts: within each run the factor drifts, and the lines sit at different heights, so it depends on the medium as well. All OD readings were taken inside the spectrophotometer's linear range (diluted and multiplied back), so the drift reflects the cells, not the instrument. And because a direct count agrees closely with CFU through this range (R² above 0.99 across six species [6]), the same holds for an OD600-to-CFU calibration. From SBT internal data.

Diluting into the linear range keeps the OD measurement itself honest [5] , but it does nothing for the biology, so the conversion factor still drifts. The CFU axis carries its own variability too, because plate counts depend on the plating method, undercount when colonies merge on a crowded plate, and scatter from plate to plate [2] . Both sides of the calibration move, which is why a curve is best understood as a snapshot, valid only for the exact strain, medium, and conditions it was built in.

### A direct count skips the correlation

The reason a calibration curve is needed at all is that OD600 does not measure cells. BactoBox® does. It counts whole bacterial cells one at a time and reports the concentration directly in cells/mL, so there is no proxy to convert and no per-condition curve to rebuild. When the medium or the strain changes, the measurement does not need recalibrating, because it was never a proxy in the first place. For how that count is produced and what it does and does not represent, see Understanding BactoBox® cell counts .

Reading cells directly does not throw away the connection to CFU. It has been measured. In a performance-qualification study across six bacterial species, BactoBox® cell counts tracked CFU on a close-to 1:1 relationship through exponential and early stationary phase, with R² above 0.99 [6] . Figure 2 shows that agreement holding across more than four orders of magnitude. So through the range where an OD600-to-CFU curve is built and used, a direct count returns the same number the plate count would, without the calibration step in between.

Figure: Figure 2. Direct counts tracked against CFU for actively growing cultures. Paired BactoBox® cell counts and CFU/mL for six bacterial species (S. epidermidis, E. coli, K. aerogenes, A. baumannii, L. innocua, P. fluorescens), through growth and early stationary phase. The points sit on the 1:1 line across more than four orders of magnitude, so across this range a direct count and a plate count read the same number. Data from the supplementary material of Jordal et al. 2025 [6].

The practical effect is that the cell number is available in about two minutes, with no operator-dependent counting step, and it stays comparable from run to run and medium to medium without a calibration to maintain. That is what makes a series of direct counts a clean way to read growth kinetics. A growth rate calculated from cells/mL is an unambiguous measure of how fast the population is dividing, which OD600 cannot give because its signal also moves with size and morphology.

### A practical takeaway

If you keep an OD600-to-CFU calibration, treat it as condition-specific, and rebuild it whenever the strain, the medium, or the cultivation conditions change. If you would rather not maintain a calibration at all, measure the cells directly and keep CFU for the questions only it answers, such as culturability or compendial release. And if you are set on tracking your runs with a calibrated OD curve, you can at least drop the plate counts and build the curve against a direct cell count instead of CFU to save workload. For more on the proxy itself and where it is and is not trustworthy, see Understanding OD600 .

### References

1. [1] Beal J, Farny NG, Haddock-Angelli T, et al. Robust estimation of bacterial cell count from optical density. Communications Biology. 2020;3:512. https://doi.org/10.1038/s42003-020-01127-5

2. [2] Martini KM, Boddu SS, Nemenman I, Vega NM. Maximum likelihood estimators for colony-forming units. Microbiology Spectrum. 2024;12(9):e03946-23. https://doi.org/10.1128/spectrum.03946-23

3. [3] Stevenson K, McVey AF, Clark IBN, Swain PS, Pilizota T. General calibration of microbial growth in microplate readers. Scientific Reports. 2016;6:38828. https://doi.org/10.1038/srep38828

4. [4] Åkerlund T, Nordström K, Bernander R. Analysis of cell size and DNA content in exponentially growing and stationary-phase batch cultures of Escherichia coli. Journal of Bacteriology. 1995;177(23):6791–6797. https://doi.org/10.1128/jb.177.23.6791-6797.1995

5. [5] Myers JA, Curtis BS, Curtis WR. Improving accuracy of cell and chromophore concentration measurements using optical density. BMC Biophysics. 2013;6:4. https://doi.org/10.1186/2046-1682-6-4

6. [6] Jordal PL, González Díaz M, Aalund F, Skands G. Performance qualification of impedance flow cytometry as a rapid in-process control proxy for colony-forming units in bacterial fermentation processes. Journal of Microbiological Methods. 2025;238:107284. https://doi.org/10.1016/j.mimet.2025.107284

## OD600-incompatible matrices

Source: https://sbtinstruments.com/knowledge/od600-incompatible-matrices

### When the medium defeats the proxy

Getting accurate growth data is important in every cultivation setting, whether the work is process development, applied research, biorefinery, anaerobic biology, or industrial fermentation. The fastest and most ubiquitous readout is optical density at 600 nm: cheap, fast, available on every benchtop. In clean broths it provides results. In some media, it does not. The matrix itself defeats the measurement, and no amount of careful blanking will rescue it.

Anaerobic broths with resazurin shift color during cultivation, putting the indicator's optical state directly in the OD600 measurement window. Lignocellulosic hydrolysates carry undigested cellulose and lignin particulates that scatter at 600 nm independently of the cells. Low-cost agro-industrial feedstocks like molasses and corn steep liquor, the staple carbon and nitrogen sources of bulk bioproduction, combine deep visible-range color from melanoidins with particulates and lot-to-lot variability that no blanking strategy can subtract. In each, the optical-density signal carries everything in the cuvette except a clean count of the cells. If your medium is one of these, or behaves like one, this article is about an alternative.

Most of the published OD600 literature is written about clear minimal media: M9 plus glucose, defined glycerol broths, standard LB. That is not where most applied research happens. Many matrices that matter for process development, fermentation scale-up, biorefinery work, and anaerobic research are richer, dirtier, and harder to read optically. When OD600 doesn't work, scientists usually fall back on slow methods such as CFU plating or dry cell weight, or accept a noisy signal and post-rationalize the curve. Neither delivers growth data trustworthy enough for decisions that depend on knowing what the culture is doing right now.

The rest of this article covers three media families where OD600 fails for well-characterized reasons, why direct impedance-based cell counts sidestep the problem, and where the limits of the BactoBox® alternative actually lie.

### Common media where OD600 breaks down

| Medium / matrix | Common applications | What defeats OD600 |
|---|---|---|
| Anaerobic media with resazurin (RCM, PYG, Wilkins–Chalgren, mGAM, Schaedler) | Clostridium , Bacteroides , Faecalibacterium , gut-microbiome research, dental microbiology, rumen microbiology | Resazurin absorbs at 600 nm; trace oxygen during sampling back-shifts the color, placing the indicator's optical state directly in the measurement window [1] . |
| Lignocellulosic hydrolysates | Biorefinery, second-generation ethanol, lignocellulosic biomass conversion. Organisms include Saccharomyces , Zymomonas mobilis , Clostridium , and mixed cultures | Cellulose fines and lignin particulates scatter at 600 nm independently of the cells; OD600 is not applicable for cultures with insoluble particles [2] . |
| Low-cost agro-industrial feedstocks (molasses, corn steep liquor, crude glycerol) | Bulk bioproduct manufacturing: baker's yeast, bioethanol, amino acid fermentation (L-glutamate, L-lysine via Corynebacterium glutamicum ), citric acid ( Aspergillus niger ), lactic acid, recombinant E. coli and Bacillus processes | Molasses carries melanoidins (Maillard products) and other colored impurities that absorb broadly across the visible range; raw molasses routinely requires decolorization pretreatment before fermentation [3] . Corn steep liquor and similar byproducts add substantial lot-to-lot variability in turbidity and composition [4] . |

The common shape across these three is that the failure is not about the cells. It is about everything else in the cuvette: the indicator, the particulates, the precipitates, the pigments, the additives. Calibration cannot rescue this, because every one of those non-cell contributors depends on the medium, the lot, the cultivation conditions, the redox state of the broth, and the timing of the sample.

Some growth-data failures look matrix-driven but are actually organism-driven. Mycoplasma species, for example, produce so little turbidity at peak growth that OD600 fails regardless of which broth they are cultivated in. The issue is the cell size and wall-less envelope, not the medium. The dedicated Mycoplasma growth data article covers that case.

Figure: Figure 1. Schematic. In a complex medium, OD600 sums every scattering and absorbing element in the optical path (left). BactoBox® classifies each particle individually by its impedance signature (right), so cells and non-cell content are separated regardless of how the medium looks.

### Why OD600 fails in these media

OD600 is an extinction measurement, not a cell count. The signal at 600 nm sums every contributor in the optical path that removes light from the beam, whether by scattering (cells, peptone fines, precipitates, antifoam droplets) or by absorbance (indicator dyes, melanoidins from caramelized sugars, other colored impurities) [5] . In the media in the table above, the non-cell contributors are either too large to ignore, too variable to subtract, or too entangled with the cells over time to separate.

Calibration cannot rescue this. A calibration that works for one batch of hydrolysate or one lot of corn steep liquor will fail on the next, because the non-cell contribution to OD changes lot-to-lot, run-to-run, and even sample-to-sample as additions, oxygen exposure, and pH corrections shift the optical state of the broth.

### What BactoBox® counts in these media

BactoBox® uses impedance flow cytometry. Each particle that crosses a pair of microelectrodes in a microfluidic channel is recorded as a single event and classified on its electrical signature. Insoluble background, such as a peptone fragment or a precipitate crystal, gives a different signature from a whole bacterial cell and is counted separately. Dissolved components, such as resazurin, sit far below the detection window and produce no signal. The measurement does not depend on the optical properties of the broth. The same cells get counted whether the medium is clear, opaque, colored, or shifting through the cultivation.

Understanding BactoBox® cell counts covers the principle, the classification logic, and the comparison with other methods in detail. BactoBox® cell counts have been benchmarked against colony-forming-unit plating across six bacterial genera in fermentation processes, with near-perfect correlation through exponential, deceleration, and stationary phases [6] .

The 0.5–5 µm calibration range still applies. In matrices with substantial particulate content sitting inside that gate, a brief check against a reference method on a new medium remains good practice. We are happy to help work through that for a specific application.

### What direct counts unlock

In matrices where OD600 is unusable, scientists typically choose between two bad workflows. Either accept slow downstream methods like CFU plating or dry cell weight at the end of the run, and design experiments around the readout latency. Or invest in elaborate sample-prep workarounds: multi-step dilutions, filter blanks, regression curves against CFU, lot-by-lot recalibration, switching to NIR or capacitance probes that work in the bioreactor but not at the lab bench.

A direct cell count in roughly two minutes per sample sidesteps both. Process development can happen in the medium the process actually runs in, with the same readout time as a clean broth. Strain comparisons in anaerobic or hydrolysate media become direct cell-density comparisons rather than turbidity-difference comparisons. Yield and productivity calculations are built on cells/mL, which is the unit downstream economics depend on already. Phase boundaries (lag, exponential, deceleration, plateau) become observable in real time, in the matrix where the process lives, on the same instrument across applications. Growth data you can trust comes from counting cells directly, not from interpreting how much light got through the broth.

### References

1. O'Brien, J., Wilson, I., Orton, T., & Pognan, F. (2000). Investigation of the Alamar Blue (resazurin) fluorescent dye for the assessment of mammalian cell cytotoxicity. European Journal of Biochemistry, 267(17), 5421–5426. https://doi.org/10.1046/j.1432-1327.2000.01606.x

2. Duedu, K. O., & French, C. E. (2017). Data for discriminating dead/live bacteria in homogenous cell suspensions and the effect of insoluble substrates on turbidimetric measurements. Data in Brief, 12, 169–174. https://doi.org/10.1016/j.dib.2017.04.003

3. Roukas, T. (1998). Pretreatment of beet molasses to increase pullulan production. Process Biochemistry, 33(8), 805–810. https://doi.org/10.1016/S0032-9592(98)00048-X

4. Wahjudi, S. M. W., Petrzik, T., Oudenne, F., Lera Calvo, L., & Büchs, J. (2023). Unraveling the potential and constraints associated with corn steep liquor as a nutrient source for industrial fermentations. Biotechnology Progress, 39(6), e3386. https://doi.org/10.1002/btpr.3386

5. Myers, J. A., Curtis, B. S., & Curtis, W. R. (2013). Improving accuracy of cell and chromophore concentration measurements using optical density. BMC Biophysics, 6, 4. https://doi.org/10.1186/2046-1682-6-4

6. Jordal, P. L., González Díaz, M., Aalund, F., & Skands, G. (2025). Performance qualification of impedance flow cytometry as a rapid in-process control proxy for colony-forming units in bacterial fermentation processes. Journal of Microbiological Methods, 238, 107284. https://doi.org/10.1016/j.mimet.2025.107284

## Mycoplasma growth data: from slow CCU and CFU to rapid direct cell counts

Source: https://sbtinstruments.com/knowledge/mycoplasma-growth-data

### A faster route to a mycoplasma growth curve

Mycoplasma cultures have always been hard to measure. The organisms are small, the media are complex, the growth is slow, and the standard quantification methods microbiologists use for mycoplasma — color-changing units (CCU/mL) from microbroth dilution, colony-forming units (CFU/mL) from agar plating, qPCR-based contamination tests — share one property in common: they take days, or longer, to read out. A growth curve sampled every few hours can take more than a week to assemble into a curve that can actually be interpreted.

BactoBox® is a benchtop instrument that produces a direct count of whole bacterial cells in roughly two minutes per sample, by impedance flow cytometry. Applied to mycoplasma cultures, it gives the same kind of cells/mL number it produces for any other bacterial culture, with the same speed. In our work with the HUN-REN Veterinary Medical Research Institute , BactoBox® cell counts were measured in parallel with CCU/mL across full growth curves for Mycoplasma anserisalpingitidis and Mycoplasma gallisepticum , and the two methods tracked each other strongly across the curve.

The rest of this article covers what the established mycoplasma quantification methods measure, what BactoBox® adds to that set, how to read a mycoplasma growth curve from direct cell counts, where the method belongs in a research and process-development workflow, and where it does not.

### Why counting mycoplasma is hard

Several things conspire. Mycoplasma cells are wall-less, fragile, and small — typically 0.3–0.8 µm in their dividing form, smaller than most bacteria and irregular in shape, with pleomorphic morphologies that resist clean optical resolution [1] . They grow slowly, in complex undefined media that often include serum and sterol supplements [1] . Doubling times are measured in hours — on the order of 6–8 hours for M. pneumoniae in rich broth — and visible growth on agar typically takes a week or more [2] . Cell densities at plateau sit close to the OD600 detection limit, and pigments in the medium dominate the optical signal at those densities anyway. The methods that work in mycoplasma cultivation labs work around these properties, but they all pay for the workaround in time.

### How mycoplasma is counted today

Five methods are commonly used to quantify mycoplasma cultures. Each addresses a different question, and each pays for it differently.

| Method | What it measures | Time to readout | Where it fits |
|---|---|---|---|
| CCU/mL (color-changing units) | Lowest serial dilution that produces a metabolic color change in the medium's pH indicator [3] | 24–48 h for fast growers; up to a week or more for slow species [3] | The most widely used quantification approach in mycoplasma research |
| CFU/mL (colony-forming units) | Culturable cells, counted from the characteristic "fried-egg" colonies on agar [1] | Days to a week | Labor-intensive; undercounts aggregated samples; misses cells that are still whole but have lost culturability |
| qPCR and other NAT methods | mycoplasma DNA copies (genome equivalents) | Hours, at the assay step | Widely used for contamination detection in cell culture [4] and for compendial release testing of biologics and cell-therapy products [5][6] ; cannot distinguish viable from non-viable [4] |
| OD600 | Optical scattering of the culture | Minutes | Signal too low at typical mycoplasma densities; pH-indicator dyes in many mycoplasma media interfere. See Understanding OD600 |
| Direct microscopy | Cells visualized in a counting chamber | Minutes per sample | Small size, low contrast, and pleomorphic morphology make routine quantitative microscopy operationally hard; used for confirmation rather than growth-curve work |

The common thread is time. CCU and CFU read out in days. qPCR is fast at the assay step but answers a different question — is mycoplasma DNA present? — not how is the population changing? OD600 and direct microscopy are fast enough but poorly matched to mycoplasma biology. A multi-point growth curve sampled every few hours can take a week or more to assemble into a form that can actually be interpreted.

### What BactoBox® counts in a mycoplasma culture

BactoBox® uses impedance flow cytometry: each particle that crosses a pair of microelectrodes in a microfluidic channel is recorded as a single event, and those that are bacterial cells are counted. The result is a cell concentration, in cells/mL, available in roughly two minutes per sample. Understanding BactoBox® cell counts covers the principle, the classification logic, and the comparison with other methods in detail.

The classification is calibrated for particles in the 0.5–5 µm range. Mycoplasma cells sit at the small end of that range and, in some preparations, slightly below it. The 0.5–5 µm specification is best understood as a rule of thumb. BactoBox® cell counts have been benchmarked against colony-forming-unit plating across multiple bacterial species in fermentation processes, with near-perfect correlation through exponential, deceleration, and stationary phases [7] . The method has been verified across multiple mycoplasma species in our work with HUN-REN on M. anserisalpingitidis and M. gallisepticum , where BactoBox® cell counts tracked CCU/mL across the full growth curve. At the small end of the size range, the J. Craig Venter Institute uses BactoBox® to acquire growth data on JCVI-Syn3.0 , the synthetic minimal-genome organism derived from Mycoplasma mycoides whose cells sit close to or below the nominal lower bound of the detection range. For a new species or a new medium, a brief check against a reference method remains good practice.

### A worked example: Mycoplasma gallisepticum

The figure below shows a full growth curve of M. gallisepticum measured in parallel by BactoBox® and the microbroth dilution method, from inoculation through 48 hours. In the animated build-up, BactoBox® cell counts appear as the measurements would become available in a real run, with each reading produced within minutes of sampling. A clock then ticks across days to convey the wait that follows every sample drawn for CCU/mL. Only at the end of that wait do the CCU/mL values appear — together, all at once, because in practice the dilution panel must finish changing color before the readout can be assigned.

Figure: Figure 1. Growth curve of Mycoplasma gallisepticum measured in parallel by BactoBox® (direct cell counts, violet) and the microbroth dilution method (CCU/mL, purple). The two series track each other across the growth curve. BactoBox® readings are available in approximately two minutes per sample; CCU/mL values are read out only after color change. Data: HUN-REN Veterinary Medical Research Institute. Full context in our customer story with HUN-REN .

The curve has the shape a mycoplasmologist would expect. A short lag through the first ~12 hours, with cell concentration drifting around 10⁶ cells/mL. A transition into exponential growth from roughly 16 to 20 hours. Rapid exponential expansion from 20 to 32 hours, where the population climbs three orders of magnitude. An early plateau by 48 hours, with cells/mL stabilizing in the 10⁹ range. BactoBox® and CCU/mL trajectories follow each other through these phases.

The difference between the two methods is when the curve is readable . The BactoBox® trajectory exists from T0 onward and is available in real time. The first CCU/mL value cannot be assigned until its corresponding microbroth-dilution panel has incubated long enough to change color, and the value associated with the sample drawn at, say, T8 is not available for at least 24 to 48 hours — and for slower-growing species, often a week or more. For a workflow that depends on knowing what the culture is doing right now — for harvest decisions, sampling-point selection, anti-mycoplasma assay readouts, or media-comparison work — direct cell counts produce a curve that does not yet exist by any other method.

### What direct counts unlock

For decades, mycoplasma growth experiments have had to be designed around a measurement that is only readable days after the sample is taken. Curves have been sparse. Phase transitions have been inferred rather than observed. Comparisons between media or strains have rested on endpoint counts rather than full trajectory shapes. None of this is a fault of the methods — CCU and CFU work as designed — but the time scale of the readout has shaped the experiments.

A direct cell count in minutes sits alongside them. CCU and qPCR remain the right tools for culturability and contamination detection, respectively. What changes is the resolution available for studying how mycoplasma cultures actually behave: phase boundaries become observable as they happen, media comparisons become full curves rather than endpoint pairs, and the kind of process-development work that has long been routine for E. coli or Bacillus subtilis becomes feasible for organisms that have, until now, been too slow to measure in almost real time.

The HUN-REN team's continuing use of BactoBox® across multiple mycoplasma species is one example of what this looks like in practice. For specific organisms, media, or workflows, the right next step is usually a short conversation about fit.

### What BactoBox® is not, for mycoplasma

BactoBox® counts whole mycoplasma cells. It is not a measure of culturability, it does not detect mycoplasma DNA, and it is not a compendial method — it is not a substitute for USP <63> [5] , Ph. Eur. 2.6.7 [6] , or any equivalent test used for biologics or cell-therapy release, and it is not a contamination test for routine cell-culture monitoring [4] . The question BactoBox® answers is the growth-curve question: how many whole mycoplasma cells are in this culture, right now . That makes it useful for research and process-development work — studying growth itself, comparing strains, developing or optimizing media, evaluating anti-mycoplasma compounds, characterizing contamination kinetics in research settings — not for regulated release or routine screening.

### Mycoplasma media: serum, background, and dilutions

Mycoplasma cultivation runs on a small family of complex undefined media [1] . Frey medium is the standard for avian mycoplasmas such as M. gallisepticum and M. synoviae [8] . Friis medium and its modified variants are used for M. hyopneumoniae and related swine species [9] . SP4 medium [10] and Hayflick medium [11] are general formulations used for M. pneumoniae and as starting points for many other species. Older formulations such as PPLO broth, and commercial preparations such as Mycoplasma Experience and MolliScience medium, sit alongside these. All share a common architecture: a rich peptone or beef-heart infusion base, a yeast-extract supplement, animal serum, and a pH indicator that shifts color as mycoplasma metabolism acidifies the medium [1] .

The serum and undefined components in these media can contribute a small background signal to BactoBox® measurements at low dilution. Two approaches handle this cleanly.

The first is to measure a sterile-medium negative control on BactoBox® and subtract the average background from each sample reading. This is the approach used in our work with HUN-REN and it produces fully background-corrected counts.

The second, simpler, approach is to avoid 1:100 dilutions of mycoplasma culture for BactoBox® measurement, and to work instead with 1:1,000 dilutions or higher. At 1:1,000 the medium background contribution falls below the BactoBox® detection threshold, and the count can be read directly as a mycoplasma cell count with no correction step. For routine growth-curve work this is the simpler workflow. For samples at the low end of the curve where further dilution would push the count below the BactoBox® working range, the background-subtraction route remains available.

### References

1. Razin, S., Yogev, D., & Naot, Y. (1998). Molecular biology and pathogenicity of mycoplasmas. Microbiology and Molecular Biology Reviews, 62(4), 1094–1156. https://doi.org/10.1128/MMBR.62.4.1094-1156.1998

2. Waites, K. B., & Talkington, D. F. (2004). Mycoplasma pneumoniae and its role as a human pathogen. Clinical Microbiology Reviews, 17(4), 697–728. https://doi.org/10.1128/CMR.17.4.697-728.2004

3. Hannan, P. C. T. (2000). Guidelines and recommendations for antimicrobial minimum inhibitory concentration (MIC) testing against veterinary mycoplasma species. Veterinary Research, 31(4), 373–395. https://doi.org/10.1051/vetres:2000100

4. Nikfarjam, L., & Farzaneh, P. (2012). Prevention and detection of mycoplasma contamination in cell culture. Cell Journal (Yakhteh), 13(4), 203–212. https://pmc.ncbi.nlm.nih.gov/articles/PMC3584481/

5. United States Pharmacopeial Convention. USP General Chapter <63> Mycoplasma Tests. United States Pharmacopeia and National Formulary (USP–NF), Rockville, MD. https://www.usp.org/microbiology

6. European Directorate for the Quality of Medicines & HealthCare. European Pharmacopoeia General Chapter 2.6.7: Mycoplasmas. Ph. Eur. 12.2, in force 1 April 2026. EDQM, Strasbourg. https://www.edqm.eu/en/-/epc-adopts-mycoplasmas-general-chapter-and-monographs-updated-to-incorporate-latest-analytical-developments

7. Jordal, P. L., González Díaz, M., Aalund, F., & Skands, G. (2025). Performance qualification of impedance flow cytometry as a rapid in-process control proxy for colony-forming units in bacterial fermentation processes. Journal of Microbiological Methods, 238, 107284. https://doi.org/10.1016/j.mimet.2025.107284

8. Frey, M. L., Hanson, R. P., & Anderson, D. P. (1968). A medium for the isolation of avian mycoplasmas. American Journal of Veterinary Research, 29(11), 2163–2171. https://pubmed.ncbi.nlm.nih.gov/5693465/

9. Friis, N. F. (1975). Some recommendations concerning primary isolation of Mycoplasma suipneumoniae and Mycoplasma flocculare. Nordisk Veterinærmedicin, 27(6), 337–339. https://pubmed.ncbi.nlm.nih.gov/1098011/

10. Tully, J. G., Rose, D. L., Whitcomb, R. F., & Wenzel, R. P. (1979). Enhanced isolation of Mycoplasma pneumoniae from throat washings with a newly modified culture medium. Journal of Infectious Diseases, 139(4), 478–482. https://doi.org/10.1093/infdis/139.4.478

11. Hayflick, L. (1965). Tissue cultures and mycoplasmas. Texas Reports on Biology and Medicine, 23(Suppl 1), 285–303. https://pubmed.ncbi.nlm.nih.gov/5833547/