> For the complete documentation index, see [llms.txt](https://help.sbtinstruments.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.sbtinstruments.com/troubleshooting/not-in-the-new-structure/divergence-from-plate-counts.md). # How do I get good correlation with plate counts? BactoBox® shows a strong [correlation with plate counts](/mpd/encyclopedia/correlation-with-plate-counts.md) for most [active cultures](/mpd/encyclopedia/active-cultures.md). But sometimes the two methods are not in perfect agreement. This is mainly because plate counts enumerates culturable cells while BactoBox® cells/mL includes additional physiological states. {% hint style="info" icon="boxing-glove" %} ### What BactoBox® counts depends on the gating setting The default gating setting, [BacTotal-10](/advanced/not-in-the-new-structure/gating/bactotal-10.md), gives a total count of whole cells. It includes live, dead, and viable-but-non-culturable cells. For active cultures, it correlates strongly with plate counts. The cell concentration resembles a microscopic cell count using total cell stains like [SYBR Green or DAPI](https://journals.asm.org/doi/pdf/10.1128/aem.01724-09). Other gating settings count a different set of objects. {% endhint %} {% hint style="info" icon="ring" %} ### Plate counts (CFUs) enumerates only culturable cells Plate counts only enumerate the cells that can multiply and form colonies on solid agar. Total viable count may be underestimated if the growth conditions are not suitable or if the bacteria have entered a [viable-but-non-culturable state](http://doi.org/10.1016/j.tim.2023.04.009). {% endhint %} The below table gives an overview of different scenarios where BactoBox and plate counts diverge. Click the links to jump to the relevant section.

Scenario	Typical symptom(s)	Mitigation
#high-particulate-background	BactoBox® is higher than CFUs in lag and acceleration phase followed by gradual convergence in exponential phase	Mitigate presence of background particulates. See Check medium background
#old-starter-culture	BactoBox® is higher than CFUs in lag and acceleration phase followed by gradual convergence in exponential phase	Improve starter culture. See Get starter culture.
#culture-is-in-decline-phase	CFUs progressively drops while BactoBox® total cells stays relatively constant	Harvest/treat culture earlier to avoid decline phase where culturability drops
#objects-are-too-big	CFUs are >50% higher than BactoBox® results.	Make the sample suitable for flow cytometry. See Break up cell clumps

## High particulate background In rare instances, particulates from the growth medium or other sources may be classified as cells. As demonstrated below, the typical symptom is an initial divergence between plate counts and BactoBox® results in lag and acceleration phase. The contribution from background particulates will usually be negligible once the culture hits exponential and stationary stage. In the below example, the measurement on bacterium-free Mycoplasma growth medium returned a background of \~3.5 × 10⁶ cells/mL. This explains the gap between cells/mL and CCU/mL during lag phase. [Check medium background](/more/how-to-guides/check-medium-background.md) shows how to measure background particulates and correct for them.

*M. gallisepticum* growth curve tracked with BactoBox® (blue) and color-changing units (grey). The red box highlights discrepancies between the two methods in lag and acceleration phases.

## Old starter culture BactoBox® and plate counts will diverge if the starter culture contains a significant proportion of non-culturable cells. This typically happens when the starter culture has progressed to the decline phase. The implications are demonstrated in the *E. coli* example below, where a relatively old starter culture was used for inoculation. BactoBox® (lavender) is higher than plate counts in lag and acceleration, while methods gradually converge through exponential phase. Regression analysis returns an R² ≈ 1 for the log transformed data in this range. This means that the correlation between methods is strong in exponential, deceleration, and stationary phase. There is a simple way to close the gap in lag and acceleration phase. Simply use a [High-quality culture](/mpd/encyclopedia/high-quality-culture.md) as starter culture. Use the workflow [Optimize starter culture](/mpd/workflows/identify-optimal-inoculum-size.md) to improve your starter culture. By doing so you will also benefit from a shorter lag phase duration.

*E. coli* shake flask growth curve. The data points represent BactoBox® (lavender) and plate counts (yellow). Triplicate repeats were done for method. The red box highlights discrepancies between the two methods in lag and acceleration phases. The right plot is a log transformation and linear regression of the data from the grey box in the left figure.

## Culture is in decline phase The below growth curve example includes the decline phase. Beyond 12 hours, the plate counts (yellow) and BactoBox® results (lavender) diverge. The grey box on the left figure highlights the acceleration, exponential, deceleration, and stationary phases. Across these stages, the lavender and yellow data points are practically superimposed. The right plot shows a regression analysis of the log-transformed data. R^{2 ≈} 1 indicates a strong correlation between BactoBox® and CFU/mL for these growth stages. As the culture hits the decline phase, cells/mL and plate counts diverge. This is because plate counts reflects culturability while the total cells from BactoBox® reflects the total concentration of live, viable-but-non-culturable, and dead cells.

*K. aerogenes* shake flask growth curve. The data points represent BactoBox® (lavender) and plate counts (yellow). Triplicate repeats were done for each method. The red box highlights discrepancies between the two methods in decline phase. The right plot is a log transformation and linear regression of the data from the grey box in the left figure.

## Objects are too big BactoBox® detects objects when they are within 0.5 - 5 µm equivalent spherical size. If objects are bigger, they are not able to enter the measurement channel. Some bacteria may form long chains or big clumps. Disaggregation[^1] is needed before the cells can be detected by BactoBox®. This is described in more detail in [Break up cell clumps](/more/how-to-guides/break-up-cell-clumps.md). {% hint style="info" %} ## Plate counts on bacterial clumps and chains Bacterial aggregates are generally a pain to measure. If your bacteria form clumps or chains, neither BactoBox® nor plate counts give meaningful results. Plate counts are measured in *colony-forming units* (CFUs). However, a *colony* does not distinguish between single cells and aggregates. What does your CFU number mean then? Answer: "It depends". Not happy with that answer? Neither are we. {% endhint %} *Bacillus* species often form chains and clumps in late exponential stage. The below data points for exponential stage follow an exponential growth model (R² ≈ 1). After the exponential growth phase, a normal growth curve would show a deceleration and stationary phase. But in this example, the cell concentration drops significantly after 6 hours. This is most likely because a proportion of the objects get to big to enter the measurement channel. This suggests that - for *B. cereus* - concentrations after the exponential stage should be interpreted with caution. Note that if the goal is to quantify viable cells, plate counts also become unreliable at this stage, as a single colony may originate from multiple cells. As demonstrated by R² ≈ 1 for the exponential growth model, BactoBox® is still useful for growth rate determination before the culture starts to chain/clump.

*Bacillus cereus* shake flask growth curve tracked using BactoBox®. Left plot includes all data points, while the plot on the right only includes the data points from exponential growth phase. The red box highlights discrepancies between the two methods in decline phase. The dashed line and regression formula represent a fitted exponential growth model.

[^1]: Break up clumps or chains of cells --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://help.sbtinstruments.com/troubleshooting/not-in-the-new-structure/divergence-from-plate-counts.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.