# SBT Instruments — full site content for LLM ingestion > This file contains the prose content of every public page on https://sbtinstruments.com, concatenated for convenience. SBT Instruments builds BactoBox®, a benchtop instrument that delivers direct bacterial cell counts in cells/mL in about two minutes using impedance flow cytometry. # Home URL: https://sbtinstruments.com/ ## Growth data you can trust Microbial process development made predictable and transparent The problem ## Decisions made on incomplete information Critical process decisions become guesswork when you infer growth from proxy methods, including optical density. Growth phases are misread. Process comparisons lose clarity. Transfer timing, harvest points, and process behaviour is harder to judge — and harder to improve. The solution ## Direct cell-count derived growth BactoBox® is a benchtop instrument. It counts bacterial cells directly in two minutes giving you real growth dynamics. No proxy interpretation. Clear decisions. Use cases ## Built for microbial process development Knowledge Bridge ## Insights from the field and the lab ## Ready to see your true growth kinetics? Book a call to discuss how BactoBox® fits into your process development workflow — or get in touch to explore your application. --- # Product — BactoBox® URL: https://sbtinstruments.com/product BactoBox® ## Build real growth curves from direct cell counts BactoBox® is a benchtop impedance flow cytometer. It counts bacterial cells directly and gives you true growth kinetics in two minutes. How it works ## Impedance flow cytometry BactoBox® electrically counts individual bacterial cells as they pass through a microfluidic channel. Advanced technology, made easy-to-use. What you see ## Your growth kinetics, visualized Every measurement feeds directly into a live growth curve. You see cell concentration over time — not absorbance units, not turbidity estimates. Actual cells/mL. Where it fits ## Designed for the process development lab Specifications ## Technical details ## Ready to see BactoBox® in action? Book a call to discuss how BactoBox® fits your workflow — or reach out to explore your specific application. --- # About SBT Instruments URL: https://sbtinstruments.com/about About SBT Instruments ## Reliable growth curves. Confident decisions. We develop and produce BactoBox® for the fermentation industry. Our customers come to us because the existing growth-tracking methods are unreliable, cumbersome, or both. With our help, your fermentation becomes both efficient and consistent. We patented our core technology back in 2017 and we now serve a worldwide customer base from our office in Denmark. We have hands-on experience with fermentations of all scales and across many real-world applications. ## SBT Instruments A/S March 25th, 2014 Symfonivej 37 2730 Herlev Copenhagen, Denmark ## Brief history The history of SBT told in a few key events. ## Annual reports All annual reports are in Danish. ## Open positions Looking for a job? See our open positions on LinkedIn. ## Want to learn more? Get in touch or explore what BactoBox® can do for your lab. --- # Customer Stories URL: https://sbtinstruments.com/customer-stories ## Customer stories --- # HUN-REN customer story URL: https://sbtinstruments.com/customer-stories/hun-ren Customer story ## Truthworthy growth kinetics for Mycoplasma HUN-REN Veterinary Medical Research Institute Research Institute, Hungary ## The challenge of the uncountable Successful process development in biomanufacturing and microbiology depends on accurate process insights. Yet for many organisms — anaerobes, mycoplasmas, and other fastidious species — enumeration is one of the hardest tasks. Traditional culture-based methods can take days or even weeks, leaving researchers with little to guide them in real time. Optical density (OD) is often used as a quicker alternative. However, OD is an indirect method that is prone to misleading results. A change in OD can come from cell morphology shifts, pigment production, or other media effects, rather than actual changes in cell number. This makes it exceedingly difficult to compare process conditions or build reliable growth models. In many cases, researchers are forced to develop processes while operating in the blind. ## The HUN-REN research institute At the HUN-REN Veterinary Medical Research Institute (Hungary), a research team led by Dr. Eszter Zsófia Nagy, Dr. Dénes Grózner, Dr. Zsuzsa Kreizinger, and Dr. Miklós Gyuranecz (MolliScience) faced exactly this challenge in their work on Mycoplasma anserisalpingitidis and Mycoplasma gallisepticum . These organisms are notoriously small and difficult to quantify with conventional methods. The standard approach, CCU/mL (color-changing units per mL), requires weeks of culture before results become available. The team needed a faster, more trustworthy way to track growth. ## The BactoBox ® solution By integrating BactoBox® into their workflow, the researchers were able to obtain direct bacterial cell counts (cells/mL) within minutes. As shown in Fig. 1, their study demonstrated a strong correlation between BactoBox® measurements and CCU/mL across the growth curve, effectively replacing weeks of waiting with near real-time results. Similar results were obtained for Mycoplasma anserisalpingitidis . Figure 1: Growth curve of Mycoplasma gallisepticum showing strong correlation between BactoBox® cell counts (cells/mL) and conventional CCU/mL measurements across the growth phases. ## Confidence in minutes, not weeks The findings gave the HUN-REN team immediate, actionable insights into their microbial processes: - Rapid feedback: Decisions could be made on the same day rather than weeks later. - Reliable growth data: BactoBox® counts tracked consistently with traditional culture results. - Clarity beyond OD: Unlike OD, which can fluctuate due to morphology changes, media conditions, or pigment effects, BactoBox® provided direct and trustworthy bacterial counts. This meant their process development work could progress with much greater confidence and predictability. ## Looking ahead The HUN-REN Veterinary Medical Research Institute has now continued its research with BactoBox® as a key part of its workflow. At SBT Instruments, we believe microbial research should not be slowed down or distorted by the limitations of traditional enumeration methods. With BactoBox®, researchers gain the clarity they need to move faster, make better comparisons, and develop processes with confidence. 2 weeks → 3 min Result time reduction Direct Cell counts replacing indirect OD Real-time Actionable growth insights ## Want similar results in your lab? See how BactoBox® can transform your microbial workflows. --- # Biose Industrie customer story URL: https://sbtinstruments.com/customer-stories/biose Customer story ## Leading CDMO accelerates process development with BactoBox® Biose Industrie CDMO, France "This collaboration will further solidify Biose's position as the world leading CDMO within the LBP space" Adrien Nivoliez CEO of Biose Industrie By integrating BactoBox® into their manufacturing, Biose is setting a new industrial standard for microbial enumeration. Rapid viable cell counts in process and release testing help accelerate the path from clinical trials to market authorization for their innovative live biotherapeutics. Biose Industrie, a leading CDMO specializing in live biotherapeutic products (LBPs), relies on BactoBox® for process development and production. Conducting thousands of measurements annually, they ensure high-quality bacterial cultivation with greater consistency and precision, optimizing biotherapeutic outcomes. ## Want similar results in your lab? See how BactoBox® can transform your microbial workflows. --- # GLBRC customer story URL: https://sbtinstruments.com/customer-stories/glbrc Customer story ## How GLBRC reduced their inoculation workflow from five days to one Great Lakes Bioenergy Research Center (GLBRC) Bioenergy Research Center, USA ## The challenge: Long and unreliable inoculations At the Great Lakes Bioenergy Research Center (GLBRC), researchers in the Experimental Fermentation Lab (EFL) working with ethanolgenic bacterial cultures involved two suboptimal steps in their inoculation-to-fermentation workflow: - The traditional inoculation procedure was time-consuming : It required a total of five days before the culture was ready to be transferred into the respirometer for fermentation. - When preparing inocula, inherent variability in the culture's physiological state sometimes led to fluctuations in growth kinetics , occasionally resulting in fermentation times that extended beyond the desired 72-hour period. To improve efficiency and standardization, the team sought to implement a new workflow that reduces preparation time and ensures the inoculum enters the fermenter in a consistent and active physiological state. ## About GLBRC The Great Lakes Bioenergy Research Center (GLBRC) is a U.S. Department of Energy-funded Bioenergy Research Center led by the University of Wisconsin–Madison. With Michigan State University and other partners, they are developing sustainable biofuels and bioproducts made from dedicated energy crops grown on marginal lands. ## Using BactoBox ® to bring kinetic insight and control To implement these improvements, EFL introduced BactoBox® as a quantitative tool for monitoring bacterial growth and standardizing inoculations. The team began by measuring growth kinetics in shake flasks, using BactoBox® to track bacterial cell concentrations over time. This allowed them to map how quickly cultures progressed through growth phases under their own conditions. They then used BactoBox® to check and standardize cell concentrations at three critical stages: - After thawing the cryovial - Following the wake-up step - Before transfer from the shake flask Armed with this insight, the researchers redesigned their inoculation workflow. By aligning bacterial growth dynamics with precise cell counts, they were able to generate a ready-to-use inoculum within just one day. Figure 1: Overview of the improved inoculation workflow optimized at GLBRC. Using BactoBox® at three key stages—after thawing the cryovial, following the wake-up step, and before transfer from the shake flask—allowed researchers to monitor and standardize cell concentration throughout the process. ## The results: Fast, consistent fermentations The impact of the new workflow was immediate. Across three independent experiments, fermentations now completed reliably within 36 hours — nearly twice as fast as before, and without the variability previously observed. The inoculum entering the fermenter was more synchronized and physiologically consistent, leading to predictable fermentation kinetics. The overall lead time from inoculation to fermentation was reduced to one day. The new workflow saves time, reduces risk, and frees up laboratory resources for other important tasks. Figure 2: Fermentation completion profiles demonstrate the robustness of the improved inoculation workflow. The existing workflow (blue) frequently failed to reach completion within 72 hours, whereas the improved workflow (red) consistently completed fermentation within approximately 36 hours. ## Looking ahead With this redesign, the EFL has a reproducible and efficient inoculation pipeline tailored to their bacterial system. BactoBox® has given the researchers a new level of control — not just over bacterial counts, but over the timing and direction of growth. The GLBRC's success illustrates what BactoBox® was designed to achieve: transforming microbial workflows from slow and uncertain into fast, reliable, and data-driven processes. 5→1 Days reduced for inoculation prep 36h Reliable fermentation completion 5× Faster than previous workflow ## Want similar results in your lab? See how BactoBox® can transform your microbial workflows. --- # Nordic Microbes customer story URL: https://sbtinstruments.com/customer-stories/nordic-microbes Customer story ## Clear insights into microbial growth reduce process variability Nordic microbes Develops and produces microbial products, Denmark ## The challenge of variability in process outputs For companies working in microbial fermentation, process variability can be a major obstacle. Even when using seemingly standardized conditions, outputs can fluctuate significantly. Understanding the causes of variability is essential for reproducibility and scale-up—but existing standard methods in microbiology are not always sufficient to capture or explain process dynamics. Nordic microbes has a mission to drive the green transition in agriculture by replacing pesticides and synthetic fertilizers with natural, microbe-based solutions. At Nordic microbes, researchers observed significant variability in their process but struggled to identify why. Their media contained molasses, resulting in a very dark, opaque medium. Optical density (OD) is often used for in-process monitoring, but OD is unsuitable in media that strongly absorb or scatter light. Without real-time insight into growth patterns, it is difficult to optimize conditions. Instead, Nordic microbes relied on colony-forming units (CFUs) to characterize the processes, but reproducibility between runs was poor. ## Nordic microbes Nordic microbes has developed a unique technology that allows them to quickly identify microorganisms adapted to local soil conditions with particularly beneficial properties. This technology, called MicrobeTRAP, is a microbial trap. When placed in the soil with the right nutrients, it captures microorganisms with the desired characteristics. Learn more about Nordic microbes on their website and their LinkedIn page . ## The BactoBox ® investigations To gain clearer insight, Nordic microbes implemented BactoBox® to track bacterial growth directly in their process development. Unlike OD, which was unusable in the opaque molasses medium, or CFUs, which are slow, labor-intensive, and prone to variability, BactoBox® delivered direct cell counts in minutes. The team carried out two separate investigations. Investigation 1 ## Comparing BactoBox® with CFUs They mapped BactoBox® growth curves against CFUs and found that BactoBox® delivered smoother and more reproducible curves, with growth kinetics consistent with a simple exponential growth model. In contrast, CFUs fluctuated both within and between runs, and the data could not be mathematically modelled using standard growth equations. Investigation 2 ## Relating BactoBox® growth to auxiliary process parameters The BactoBox® intact cell concentration (ICC) growth curve was then compared to online parameters, including pH, headspace CO₂ levels, and dissolved oxygen. These changes were expected to align with distinct growth stages such as the onset of exponential and stationary phases. In reality, when compared with actual bacterial concentrations from BactoBox®, the parameter shifts did not align with growth patterns. This highlighted how critical it is to have a direct measurement of bacterial concentration rather than relying on indirect proxy methods. Figure 1: Growth curve measured with BactoBox® (left) alongside recorded online sensor parameters (right). Dashed lines indicate distinct events marked by changes in pH, CO₂, or dissolved oxygen. ## Clearer insights into variability By using BactoBox®, Nordic microbes gained a more reliable picture of their fermentation dynamics: - More reproducible data: BactoBox® revealed consistent growth curves, unlike the variable CFU counts. - Clarity in opaque media: The BactoBox® detection principle works even in very dark, opaque, and particle-rich media where OD cannot be applied. - Deeper understanding: The team confirmed that auxiliary parameters (pH, CO₂ production, DO, agitation) reflect process events but cannot be considered reliable proxies for microbial growth. This gave the team a new level of confidence in analyzing their process variability, allowing them to separate true biological behavior from artifacts of measurement. ## Looking ahead The team at Nordic microbes has now continued its work with BactoBox® as a central tool for understanding fermentation variability and supporting process scale-up. For any laboratory working with turbid or dark media—or where OD and auxiliary parameters cannot be trusted to reflect microbial growth—BactoBox® offers a way to bring speed, clarity, and confidence into process development. At SBT Instruments, we believe microbial research should not be slowed down or distorted by the limitations of indirect measurement methods. With BactoBox®, researchers gain the clarity they need to make better comparisons, move faster, and develop processes with confidence. Clarity Beyond OD and CFUs Direct Cell counts in opaque media Reproducible Consistent growth curves ## Want similar results in your lab? See how BactoBox® can transform your microbial workflows. --- # Knowledge hub URL: https://sbtinstruments.com/knowledge ## Knowledge --- # Understanding BactoBox® cell counts URL: https://sbtinstruments.com/knowledge/understanding-bactobox-cell-counts Principle and considerations ## Understanding BactoBox® cell counts Direct cell counts are new in many bacterial cultivation labs. When BactoBox® arrives in a workflow built around OD600 and CFU and produces a number called cells/mL, a fair early question is whether that number is anything more than another OD reading. This article explains what a BactoBox® cell count is, how it compares to existing methods, and what direct cell counts make possible. ## What is BactoBox® and impedance flow cytometry BactoBox® is a benchtop instrument that counts bacterial cells one 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. Every particle within the instrument's 0.5–5 µm detection range is recorded as a single event. A bacterial cell with an intact lipid membrane and a conductive cytoplasm produces the characteristic signature . A lysing cell whose envelope has been compromised does not produce that signature, so it is recorded as a particle rather than as a cell. Salt crystals, antifoam droplets, and cellular debris in the same size range are also recorded as particles. The result BactoBox® reports as cells/mL is the concentration of structurally intact total cells in the sample. This is what the instrument is producing every time the reading appears. One detail follows from the way the measurement works. Each particle that crosses the measurement zone is recorded as a single event. A doublet, a small chain, or a clump of cells therefore registers as one event with a larger electrical signature, rather than as several events. Size grows with aggregate size; the count does not. For samples that aggregate routinely, an accurate cell count requires sample preparation that breaks aggregates apart, and we work with users on the deaggregation steps appropriate for their organism and process. ## Comparison to other enumeration methods Before working through what cells/mL is useful for, it is worth being precise about how it relates to the methods most labs already run. A common misconception is that any total cell count is equivalent to any other, and that BactoBox® is, at best, a different way of producing the same kind of biomass-related signal that OD600 produces. This is not the case. Each method below answers a slightly different question, and once those differences are clear, the place BactoBox® fills in the measurement stack becomes clearer too. One framing that recurs through this section is worth flagging in advance. None of the methods discussed here measure live cells. "Live" is not an experimentally defined property at the level of an individual bacterial cell, and no microbiological method, including BactoBox®, defines or measures it operationally . What the various methods measure are specific, well-defined properties: structural integrity, membrane integrity, culturability under chosen conditions, and biomass-related optical signal. These are different properties, and the differences matter. ## BactoBox® vs. OD600 OD600 is the optical density at 600 nm read on a spectrophotometer, and it is the workhorse real-time 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. BactoBox® and OD600 therefore answer different questions, even when the curves they produce look similar in shape. The article Understanding OD600 covers the signal, its uses, and its limitations in depth. ## BactoBox® vs. CFUs 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. This is a narrower property than structural integrity. Cells that are physically intact but have lost the ability, perhaps temporarily, to grow do not register as CFUs, including cells in the viable-but-non-culturable (VBNC) state and cells that have suffered sub-lethal damage . CFU also undercounts samples containing aggregates, where a clump of multiple cells grows up as a single colony , and is sensitive to the plating workflow itself: method-dependent differences between pour plating, spread plating, and dehydrated film methods such as Petrifilm have been documented on the same samples . A BactoBox® count and a CFU count therefore measure overlapping but non-identical populations. In exponential and early stationary phase, where most structurally intact cells are also culturable, the two track each other closely, with R² values above 0.99 validated against CFU for E. coli and five additional bacterial species in those phases . Outside that window, the two diverge. The region where they diverge is classically described as the decline or death phase, but that label is itself an interpretation rather than an observation. From a comparison of structurally intact cell counts and CFU counts alone, it is not possible to determine whether cells that have lost culturability are dead or whether they have entered the VBNC state, in which they retain physical integrity but no longer form colonies on standard media. The accurate, more limited statement is that not all cells in the sample are culturable any more. The size of the gap between the two counts carries information about both the process and the analytical workflow regardless of which interpretation applies. ## BactoBox® vs. direct microscopy Direct microscopy with a counting chamber such as a Petroff-Hausser or hemocytometer is the textbook reference for counting cells directly. What is reported depends on the preparation. 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 PI/SYTO 9 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, the counted sample volume is small (typically 1 to 100 nanolitres per field). Operator variability is usually the factor hardest to ignore: two analysts counting the same chamber will not always produce the same number, and the count 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 24% off from reference values , within the chamber manufacturer's own stated 20–30% expected count discrepancy. ## BactoBox® vs. fluorescent flow cytometry Fluorescent flow cytometry shares its architecture with BactoBox®: cells pass one at a time through a sensing region, and each cell is detected as an event. The difference is the sensing. Fluorescent flow cytometry reads light scatter and the fluorescence of bound dyes, while BactoBox® reads electrical signals. What the technique reports therefore depends on the dyes used. The most common viability-style combination in bacterial work is propidium iodide (PI) paired with SYTO 9. PI cannot cross an intact bacterial membrane, so cells that take up PI are reported as having compromised membranes . PI flags as compromised any cell whose membrane has been damaged enough for the dye to enter. BactoBox®, by contrast, detects the gross electrical signature of a structurally intact cell. A cell with small membrane lesions that still maintains its overall electrical character can pass the BactoBox® classification while being flagged as compromised by PI. The two methods therefore both measure structural integrity, but at different sensitivities, and the populations they classify as structurally intact are not identical. ## What direct cell counts make possible Cells/mL from BactoBox® is a measurement of a specific, well-defined property: the concentration of structurally intact bacterial cells in the sample. The signal corresponds directly to what scientists usually mean by growth, namely cell division: a direct count rises when, and only when, division actually happens. This makes a direct cell count uniquely well-suited to growth-related measurements. Any new object that appears in the sample over time 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. 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 cells that have lost structural integrity stop being counted. Loss of culturability often comes before lysis, however, so the drop does not, on its own, reveal whether or when those cells lost culturability; a direct cell count and a CFU count read together describe the transition from loss of culturability to loss of structural integrity cleanly. 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 structurally intact cells is a different kind of measurement than the methods most cultivation labs have built their workflows around. It is not a faster OD600 and it is not a faster CFU. It answers a question those methods were not designed to answer: how many structurally intact bacterial cells are in the sample right now. BactoBox® produces that count in as little as two minutes per sample, with no operator-dependent counting step. CFU, by comparison, takes a day or more and adds variance from manual counting. The BactoBox® result is therefore fast enough to be repeated through a cultivation, and consistent enough that two operators running the same sample will report the same number. What this enables, when integrated into a process development workflow, is a clearer view of the dynamics that drive most 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. We encourage close collaboration on that transition. Other articles in our help center walk through specific applications, including Improving harvest decisions , where the end-of-fermentation comparison between BactoBox® and CFU is used as an entry point for yield improvement. You are also always welcome to reach out to us directly. ## References ## See what direct cell counts reveal in your process BactoBox® delivers direct cell counts in cells/mL in about two minutes per sample. --- # Understanding OD600 URL: https://sbtinstruments.com/knowledge/understanding-od600 Principle and considerations ## Understanding OD600 ## 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. More cells, larger cells, and more particles in the path all increase the reading. 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. 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. ## Considerations and limitations OD600 is a biomass-related signal, not a cell count, and the gap between the two has practical consequences. 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. OD cannot tell them apart from cells. 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. 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. OD600 measures turbidity, and any cell or fragment that remains structurally intact contributes to that signal regardless of which state it is in. After lysis, the released debris continues to contribute until it settles or is degraded. There is no single OD600 reading at which cells stop counting toward the signal. OD600 reports an arbitrary number. Without a calibration curve specific to the organism and the instrument — and revalidated when the medium changes — OD600 is not convertible to cells/mL or any standard biological unit. 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. 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. ## Alternatives Several methods coexist with OD600 in cultivation labs. Each answers a slightly different question. ## Common behavior 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. Each of those changes alters how the cell scatters light at 600 nm even though no new cells have been added. 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. 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. 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 intact 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. Can I use OD600 to compare different media, strains, or process conditions? The entire industry does, not without success, but OD600 is not the best method for this. Even after blanking the medium, OD600 still depends on cell size and intracellular composition — and both of those properties are themselves 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. There is no single calibration factor that survives a change in medium or strain, because the OD-to-cell ratio depends on cell properties that the new condition has changed. The error is silent: the OD curve looks clean, the ranking is internally consistent, the result is reproducible — and the ranking can still be wrong, with no diagnostic from inside OD that tells you when. The only way to compare cell yields across media, strains, or process conditions reliably is to count cells directly. We cover this in detail in our article on rational media selection . 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. ## Closing OD600 remains fast and convenient, but it is not a cell count. For decisions that depend on knowing how many cells are in the broth — when to harvest, how strains compare, whether a run is reproducible — methods that count cells directly answer the question OD was never designed to answer. ## References ## Ready to move beyond OD600 See how BactoBox® delivers direct cell counts in cells/mL in about two minutes per sample. --- # Picking the optimal medium URL: https://sbtinstruments.com/knowledge/picking-optimal-medium How direct cell counts reveal the medium that actually produces the most bacterial cells ## Picking the optimal medium ## The decision you make once Most batch bioprocesses are run for years on a single medium. The choice is made early in development, typically from a small screen of candidate media, and once it is locked into a validated process it is rarely revisited — revisiting it means re-validating. The cost of getting that choice wrong is therefore not paid once. It is paid every batch, on every fermenter, for as long as the process runs. 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. 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. 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 , a benchtop device that uses impedance flow cytometry to count bacterial cells one at a time and report a concentration in cells/mL. 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. The next plot adds those measurements alongside the OD bars on the same flasks. ## The same screen with direct cell counts 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. 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. 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. 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. 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. 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. Cell concentration in a batch cultivation rises, peaks, and falls; 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. A scientist relying on the OD curve to time a CFU sample may take it before or after the cells/mL 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 bioprocess is committed to a medium once, and that medium runs every batch for as long as the process runs. 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. Medium cost is one input line. Plant capacity is a fixed cost paid forever. 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 ## Ready to make media decisions on cells, not proxies See how BactoBox® gives you direct cell counts in cells/mL across every flask in your screen. --- # Improving harvest decisions URL: https://sbtinstruments.com/knowledge/improving-harvest-decisions How a direct cell count compared with CFU sharpens the harvest decision ## Improving harvest decisions 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 structurally intact cells and a CFU count measure different properties of the population , 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, what different patterns of agreement or disagreement reveal, and how each pattern can be turned into an opportunity to improve harvest timing and yield. The interpretive principles below apply to any direct count of structurally intact 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 . 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 harvest timing 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 harvest is typically timed for late exponential or early stationary phase, where most structurally intact cells are also culturable. In this window, BactoBox® and CFU track each other closely . An end-of-fermentation comparison that lands in close agreement is therefore the expected outcome and confirms that the harvest timing is reasonable. When the two counts disagree, the disagreement is informative — it points to a problem with either the harvest timing or the analytical workflow. 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 . The same OD value is consistent with multiple states of the underlying culture, including states that imply very different harvest decisions. ## 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 intact cells are still culturable, typically during exponential or early stationary phase . The measurement itself does not point to an immediate problem, but it does not rule out a subtler one — the harvest may be earlier than it needs to be. Agreement between the two counts confirms that the timing falls in the window where they should agree, but not that the timing is at the cell-count peak. The harvest may sit somewhere on the climb toward the plateau rather than at the plateau itself, and there may be more yield available by waiting. Verifying that the harvest sits at the plateau requires a sequence of measurements through the slowdown into early stationary phase. That is the broader argument made at the close of this article. ## Pattern 2 — the BactoBox® count is higher than the CFU count The sample contains more structurally intact cells than culturable cells. There are two distinct explanations, and both are worth investigating because 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 appear physically intact while no longer forming colonies on the plate. From the comparison alone it is not possible to determine whether those cells are dead or have entered a viable-but-non-culturable (VBNC) state , but either interpretation leads to the same operational conclusion — not all cells in the sample are culturable any more. In practical terms, an earlier harvest 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 . 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 harvest. 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 . 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 . 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. ## The bigger opportunity: map the slowdown and plateau A pattern that recurs across bacterial processes is that the harvest point is not the optimal one. Teams often harvest based on elapsed time, visual cues, or an OD600 reading, without direct visibility into when the cell count actually peaks. Because OD600 also responds to cell size and intracellular composition, the OD plateau and the cell-count peak rarely line up exactly. The end-of-fermentation comparison against CFU is a useful entry point, but the larger opportunity for yield improvement lies in mapping the transition from late exponential into early stationary phase directly. Rather than rely on a single endpoint comparison, take cells/mL measurements at intervals through this region to identify the point at which the cell count reaches its plateau. Take a CFU sample at that point. This approach establishes when to harvest, and provides a controlled comparison between the direct count and CFU at a well-defined point in the process. If the two counts disagree at the plateau, the disagreement is informative and worth investigating using the patterns above. A harvest decision grounded in the shape of the growth curve is more reliable than a harvest decision made from a single endpoint reading. ## Conclusion A single end-of-fermentation comparison, read carefully, can already point to where yield is being left on the table. Agreement between BactoBox® and CFU confirms the harvest sits in a sensible window, but does not confirm it sits at the cell-count peak. A BactoBox® count higher than CFU points to a culture that has passed its culturable peak or to incomplete colony recovery on the plate. A BactoBox® count lower than CFU points to aggregation, with implications for how CFU itself should be interpreted. The larger gain, in every case, comes from moving from a single endpoint reading to a series of readings through the slowdown and plateau. With cells/mL measured at intervals, the harvest decision becomes a decision grounded in how the population is actually behaving over time — not in elapsed time, OD600, or a delayed plate count. End-of-fermentation measurement is a good place to start. It is rarely the best place to stop. 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 ## Ready to base harvest decisions on direct cell counts See how BactoBox® delivers direct cell counts in cells/mL in about two minutes per sample. --- # Contact URL: https://sbtinstruments.com/contact ## Get in touch with SBT Instruments For questions about BactoBox®, support, technical inquiries, or quotes, please use the form below or reach us at the contacts listed. ---