Viable cell density and viability help scientists interpret culture growth, assess process disturbances, and plan feeding or harvest. Their usefulness depends on when measurements are taken and how quickly results become available.
In a manual workflow, an operator collects a sample, transfers it to the laboratory, prepares any required dilution, runs the analyzer, and records or imports the result. A cell count itself may be quick. The practical delay can arise between these steps, especially when several reactors share one operator or analyzer.
Long gaps outside normal working hours can also hide changes between observations. A morning result may show that the culture changed overnight without showing when that change began. Increasing manual sampling frequency requires additional staff time and repeated handling.
Online sampling makes a different monitoring schedule practical. Samples can be taken at defined intervals and sent directly to an integrated analyzer, with results transferred to the process information system. Scientists can then examine a more detailed time course and spend less time carrying out repetitive collection and recording tasks. The benefit is particularly relevant when following several cultures in parallel. [5]
Automation can cover different parts of the workflow. A system that fills cooled vials improves collection coverage, while analysis may still happen later. A connected online workflow also transfers the sample to the analyzer and returns the analytical result.
The distinction matters when selecting equipment. A sample collected at midnight and analyzed the next morning adds an overnight observation to the historical record. A sample measured soon after collection can also inform decisions while that phase of the process is still underway.
|
Workflow |
Practical advantage |
Main consideration |
|
Manual collection with separate analysis |
Flexible access to established laboratory methods |
Requires operator coordination and sample handling |
|
Automated collection into stored vials |
Extends collection coverage and standardizes timing |
Results remain dependent on later analysis and demonstrated storage stability |
|
Automated collection with direct analyzer transfer |
Reduces handoffs and supports timely analytical data |
Requires verified fluid transfer, analyzer coordination, and data integration |
Manual samples can also be analyzed immediately at-line. The appropriate comparison therefore uses the laboratory’s actual collection-to-result time, staffing requirements, and measurement performance.
A cell count describes the material that reaches the analyzer. If that material differs from the culture in the vessel, an otherwise well-performing analyzer can report a misleading result.
Sampling location, mixing conditions, aggregates, and settling can affect representativeness. Material remaining in a line between events may differ from freshly collected broth. These risks become relevant when concentrations change substantially during cultivation or when long intervals separate samples.
The sampling procedure should therefore define how the line is refreshed and how a representative aliquot is obtained. Initial commissioning should include comparisons after different idle periods and at relevant culture densities. The fluid path should be assessed in the configuration that will actually be used, including its tubing length and sampling connection.
Automated execution helps repeat a suitable procedure consistently. Evaluating the procedure remains necessary: repeatable measurements can still contain systematic bias. The cell-counting measurement literature distinguishes precision from accuracy and provides methods for investigating performance across dilution levels. [1]
For cell density and viability measurements, the analyzer needs a cell-containing sample. A cell-removal filter belongs in a separate pathway when the target is a soluble analyte, such as a metabolite or product.
Transport and preparation should preserve the cell population relevant to the assay. Potential influences include residence time, cell settling, aggregates, contact with surfaces, and the operating conditions of the transfer mechanism. These influences should be investigated for the intended cell type rather than assumed from pump type or nominal flow rate alone.
High-density cultures may require dilution to remain within the analyzer’s usable range. Reproducible dilution and mixing can reduce repetitive pipetting and help standardize preparation across operators and time points. The diluent, dilution factor, and time before measurement must suit the cells and the assay.
Automated flow-cytometry literature describes the importance of coordinating sample transport, dilution, mixing, and analytical timing. Its engineering lessons are useful for integrated cell analysis, while individual settings remain method-specific. [3]
An aseptic sampling strategy protects the culture from microbial contamination. Carryover is a different issue: material from an earlier sample can influence a later measurement, including in a system whose culture remains uncontaminated.
A suitable workflow addresses both. Connections and cleaning procedures must protect the process, while transfer and rinse sequences must prevent unacceptable residual cells, reagents, or cleaning fluid from affecting the next measurement. High-to-low concentration sequences are useful challenges for carryover evaluation.
Sampling volume also deserves attention. The total culture removed includes the analytical sample and any broth consumed in line refreshing or other preparation steps. For small reactors, frequent sampling can alter working volume appreciably. Compare total withdrawal per reactor over the full experiment, alongside data frequency and analytical needs.
NIST identifies sampling, sample preparation, data collection, and analysis as potential contributors to cell viability measurement error. Automating sample collection addresses only part of that chain. [4]
Analyzer settings also matter. Staining conditions, image acquisition, cell-size thresholds, debris discrimination, and aggregation handling can affect the reported result. Cadena-Herrera and colleagues evaluated manual, semi-automated, and automated counting methods and showed the importance of method-specific validation. [2]
A practical verification plan should answer the following questions:
Do paired online and comparison samples agree across the intended concentration and viability ranges?
Is the dilution process reproducible and suitable for the cell preparation?
Do sample residence time and intended holding conditions affect the result?
Is carryover acceptable after concentrated or otherwise challenging samples?
Does the workflow remain reliable after idle periods and during extended unattended operation?
Are failed transfers, incomplete measurements, and dilution changes visible in the data record?
Define acceptance criteria before the comparison. Examine systematic differences and variability rather than relying only on a high correlation coefficient. Where possible, separate sampling effects from analytical effects by measuring split aliquots under matched conditions. A manual method is a comparison method whose own performance must also be understood.
A Securecell–ZHAW application note describes online CHO culture monitoring using Numera, Cedex HiRes, and Lucullus. In the reported configuration, a complete cycle covering sampling, direct transfer, analysis, and cleaning took less than 15 minutes. Analytical results were transferred automatically to Lucullus, and online measurements were compared with manually sampled offline measurements. [5]
The note also identifies incomplete sample-loop filling and gradients in the transfer line as possible contributors to measurement variation. This is useful implementation evidence: transfer conditions belong in performance verification. The work used an earlier Numera configuration and should not be read as a guaranteed cycle time for every Numera 3 installation.
For current projects, Numera 3 provides configurable sampling and preparation with dilution up to 1:100 and routing to up to six analyzers, including Beckman Coulter Life Sciences Vi-CELL BLU analyzer. The configuration can support cell-containing analysis alongside separate preparation for other assays. The chosen analyzer, fluid interface, and method determine the implementation. [6]
When evaluating online cell counting, ask suppliers to demonstrate the complete path from the actual reactor connection to a usable result. Establish the sampling schedule per reactor, total culture withdrawal, dilution performance, analyzer capacity, and how measurements enter the process record.
For a multi-reactor laboratory, repeat this assessment with the intended number of active reactors and analytical methods. A short individual sampling cycle does not establish the achievable measurement interval for every vessel in a shared system.
Numera and Lucullus can bring collection, preparation, analysis, and process information into a coordinated workflow. The practical objective is a dependable stream of cell culture measurements that scientists can use while the experiment is running.
Discuss your reactor setup, cell concentration range, and analyzer requirements with Securecell to define an appropriate online sampling configuration.
Does automated sampling improve cell-count accuracy?
It can reduce variability associated with timing, transfer, dilution, and manual recording. Accuracy must be demonstrated for the complete sampling and analytical method under the intended operating conditions.
Can cooled samples replace immediate online viability measurements?
Cooled storage is useful when stability has been established for the intended assay and holding time. It does not establish that live-cell concentration and viability remain unchanged. Timely online analysis reduces reliance on a storage interval.
Should samples be filtered before cell counting?
Cell-removal filtration is inappropriate when the purpose is to count the original cell population. Whole-cell counting and cell-free chemistry analysis require suitable separate sample pathways.
Discover how automated sampling and integrated analytics can improve bioprocess monitoring, efficiency, and control.