Evidence map›Paper›PMID 26742110›Full record

ArticlePloS one2016

Stochastic Measurement Models for Quantifying Lymphocyte Responses Using Flow Cytometry.

Andrey Kan, Damian Pavlyshyn, John F Markham, Mark R Dowling, Susanne Heinzel, Jie H S Zhou, Julia M Marchingo, Philip D Hodgkin

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Identifying Noise Sources governing cell-to-cell variability.Current opinion in systems biology · 2018
    Article
  2. Nongenetic origins of cell-to-cell variability in B lymphocyte proliferation.Proceedings of the National Academy of Sciences of the United States of America · 2018
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Andrey KanDivision of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
Damian PavlyshynDivision of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
John F MarkhamDivision of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
Mark R DowlingDivision of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
Susanne HeinzelDivision of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
Jie H S ZhouDivision of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
Julia M MarchingoDivision of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
Philip D HodgkinDivision of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adaptive immune responses are complex dynamic processes whereby B and T cells undergo division and differentiation triggered by pathogenic stimuli. Deregulation of the response can lead to severe consequences for the host organism ranging from immune deficiencies to autoimmunity. Tracking cell division and differentiation by flow cytometry using fluorescent probes is a major method for measuring progression of lymphocyte responses, both in vitro and in vivo. In turn, mathematical modeling of cell numbers derived from such measurements has led to significant biological discoveries, and plays an increasingly important role in lymphocyte research. Fitting an appropriate parameterized model to such data is the goal of these studies but significant challenges are presented by the variability in measurements. This variation results from the sum of experimental noise and intrinsic probabilistic differences in cells and is difficult to characterize analytically. Current model fitting methods adopt different simplifying assumptions to describe the distribution of such measurements and these assumptions have not been tested directly. To help inform the choice and application of appropriate methods of model fitting to such data we studied the errors associated with flow cytometry measurements from a wide variety of experiments. We found that the mean and variance of the noise were related by a power law with an exponent between 1.3 and 1.8 for different datasets. This violated the assumptions inherent to commonly used least squares, linear variance scaling and log-transformation based methods. As a result of these findings we propose a new measurement model that we justify both theoretically, from the maximum entropy standpoint, and empirically using collected data. Our evaluation suggests that the new model can be reliably used for model fitting across a variety of conditions. Our work provides a foundation for modeling measurements in flow cytometry experiments thus facilitating progress in quantitative studies of lymphocyte responses.

Indexed as

Models, StatisticalAnimalsB-LymphocytesCD8-Positive T-LymphocytesCell DifferentiationCell DivisionEntropyFlow CytometryMiceMice, Inbred C57BLMice, TransgenicStatistical DistributionsStochastic Processes

Identifiers

PMID26742110
PMCPMC4704825

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.