Best Practices in MSC Culture: Tracking and Reporting Cellular Age Using Population Doubling Level (PDL) and not Passage Number

There has been much discussion in the literature and the blogosphere (here, here, and here) lately about keeping track of cellular age, and there is very good reason for this. First, there are multiple regulatory guidelines that propose tracking the age of cells used in biologics manufacturing. Secondly, it is well documented that cell phenotype and function can be compromised the older a cell is. A few examples from the literature as this relates to human Mesenchymal Stem Cells (hMSCs) are:


  • Nikbin shows loss of adipogenic and osteogenic differentiation of hMSCs with increasing cumulative population doublings here
  • Lo Surdoand Bauer show here that while flow marker expression is stable, there is a decrease in proliferation rate and a loss of adipose differentiation in hMSCs from passages 3 to 7.
  • Le Blanc retrospectively proposes herethat hMSCs from passages 1 or 2 are more therapeutically functional in GvHD than hMSCs from “later” passage 3 or 4 cells (the later passage cells were also cryopreserved).




In many research laboratory environments, cellular age is most often tracked by the number of times a cell has been passaged (such as in the papers above, excluding Nikbin). However, Passage Number is quite imprecise and not very acceptable as one gets into regulated environments such as translational clinical activities. It is generally accepted that tracking the Population Doubling Level (PDL) or Cumulative Population Doublings (CPD) of primary cells is a best practice on understanding cellular age in vitro. It is the goal of this blog post to help to explain how Passage Number and PDL are related, and how varying cell culture techniques can create a divergence in the reporting of Passage Number, versus PDL. We also aim to provide guidance and tools to help labs adopt the best practice in tracking PDL of their cell cultures to help bring standardization to their own experimental protocols and the field.


Regulatory Guidelines Propose Tracking Population Doubling Levels


There are pharmaceutical regulatory guidelines such as the ICH Q5D (Titled “Derivation and Characterization of Cell Substrates Used for Production of Biotech/Biological Products” that state “For diploid cell lines possessing finite in vitro lifespan, accurate estimation of the number of population doublings during all stages of research, development, and manufacturing is important.” However, Bauer states in a
review that most clinical INDs with hMSCs still do not report PDL. As early-stage cellular therapies turn the corner into later-stage clinical trials and more “product development” activities, we should see an increase in programs where PDL is tracked in a more precise manner.


Another important point from a separate guidance to note: “The population doubling level of cells used for production should not exceed an upper limit based on written criteria established by the manufacturer” which is from “Points to Consider in the Characterization of Cell Lines Used to Produce Biologicals”. Thus, if you are to use cells clinically, the regulators are going to ask that you define experimentally, backed up with data, the maximum PDL that will be used for clinical use. Lack of data in this area will likely not keep one out of a Phase 1 trial, but the further the product progresses in development and the clinical pipeline, this type of information is typically mandatory.


These guidelines are only one reason to keep track of PDL. Since it is well documented that PDL impacts cell function, in order to drive consistency into experiments, it has become a best practice to perform experiments with cells in a similar range of population doublings where the cell function of interest is still robust– be it secreted cytokines, multi-lineage differentiation, or the ability to modulate immune function. For hMSCs, we find that most researchers report performing experiments with cells in the passage range of 4 to 6. With a standard hMSC culturing protocol where you get 2.5-3 population doublings per passage, this results in an hMSC in a PDL range of 12- 18. The experimentalist thus has a challenge of using primary cells in controlled experiments where the cumulative PDL is still relevant to the function of interest.


Where Passage Number and PDL Diverge


The process of culturing cells, including hMSCs, can vary greatly between labs and will dramatically impact the number of population doublings your cells go through per passage. To illustrate this, we will use 3 representative culture processes listed below (and outlined in the table below):


  1. A “traditional” hMSC culture method of seeding cells at a density of ~5000 cells/cm2 and harvesting at ~80% confluence (which is usually ~20,000 cells/cm2) will lead to hMSCs doubling twice per passage (5000 to 10,000, then 10,000 to 20,000 – or 2 doublings per passage);
  2. While a lower seeding density of 1250/cm2 will produce 4 doublings per passage (assuming the same harvest density);
  3. And a hyper-low seeding density of 78 cells/cm2 will produce 8 population doublings per passage. – Please see the summary chart below.
Since the seeding density and harvest density can vary greatly between labs, reporting passage number is not a standardized means of reporting cell age. For example, if the goal is to use cells at a Population Doubling Level between 12 and 16 every time an experiment is performed in an attempt to standardize results, then you must use cells within 6-8 passages from culture process #1 above, within 3-4 passages using the intermediate-density culture process #2 above, and only at passage 2 using the hyper-low density seeding of cell culture process #3 above. We have attempted to outline the impact on cumulative population doublings per passage based on these 3 different methods in the chart below.


Passage 2 cells from a lab using cell culture process #1 are clearly not the same cellular age as Passage 2 cells from lab culture processes #2 or #3. Furthermore, cell culture is performed on the human’s schedule, not the cells. Cells are often harvested earlier or later due to scheduling conflicts, illness, weekends, etc. These “small” changes in timeframe can lead to large variations in PDL and resultant experimental outcomes. And importantly, since PDL are often not tracked, these details are lost, and experimental outcomes cannot be evaluated based on differences in PDL.


So How do you Calculate PDL?


The ATCC website contains the following: “…Passage number simply refers to the number of times the cells in the culture have been subcultured, often without consideration of the inoculation densities or recoveries involved. The population doubling level (PDL) refers to the total number of times the cells in the population have doubled since their primary isolation in vitro.” Unfortunately, hMSCs are a rare population in bone marrow and it is very difficult to estimate the starting number of hMSCs in the initial culture. By convention, most labs start counting hMSC cumulative population doublings after the P0 cell harvest. Furthermore, PDL is not designed to take into account the number of times these cells have divided In Vivo, and this is where donor age and health comes into play as an important variable to monitor.


To calculate the PDL of your cell cultures, you can use the equation below which is a variation of the one found on the ATCC link above:


Take Home Message


Taken together, it is clear that the best way to standardize reporting of cellular age is to report the exact PDL of cells going into the experiment and not simply the passage number of the cells. Thus, RoosterBio reports the exact PDL of each lot of MSCs we provide our customers so that they can keep track of PDL during their own experiments. We have also created a PDL vs Passage Number PowerPoint file (available on our website) and an Excel Template PDL Tracker that anyone can request for their own purposes (Please email us at We find that using cells within a consistent PDL range helps to standardize our experiments, and is a best practice that is being adopted and supported by many in order to standardize practices in hMSC culture expansion.


We are very interested in hearing other people’s suggestions on tracking PDL and driving consistency into their experiments, as well as best practices that their labs have adopted. We hope to hear from you in the comments!


Priya Baraniak
Priya Baraniak


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