Distinguishing different modes of growth using single-cell data

  1. Prathitha Kar
  2. Sriram Tiruvadi-Krishnan
  3. Jaana Männik
  4. Jaan Männik  Is a corresponding author
  5. Ariel Amir  Is a corresponding author
  1. Harvard University, United States
  2. University of Tennessee, United States

Abstract

Collection of high-throughput data has become prevalent in biology. Large datasets allow the use of statistical constructs such as binning and linear regression to quantify relationships between variables and hypothesize underlying biological mechanisms based on it. We discuss several such examples in relation to single-cell data and cellular growth. In particular, we show instances where what appears to be ordinary use of these statistical methods leads to incorrect conclusions such as growth being non-exponential as opposed to exponential and vice versa. We propose that the data analysis and its interpretation should be done in the context of a generative model, if possible. In this way, the statistical methods can be validated either analytically or against synthetic data generated via the use of the model, leading to a consistent method for inferring biological mechanisms from data. On applying the validated methods of data analysis to infer cellular growth on our experimental data, we find the growth of length in E. coli to be non-exponential. Our analysis shows that in the later stages of the cell cycle the growth rate is faster than exponential.

Data availability

All data generated during this study are deposited in Dataverse-:Kar, Prathitha; Tiruvadi-Krishnan, Sriram; Männik, Jaana; Männik, Jaan; Amir, Ariel, 2021, "Distinguishing different modes of growth using single-cell data", https://doi.org/10.7910/DVN/BNQUDW, Harvard Dataverse, V1

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Prathitha Kar

    Department of Chemistry and Chemical Biology, Harvard University, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4091-6860
  2. Sriram Tiruvadi-Krishnan

    Department of Physics and Astronomy, University of Tennessee, Knoxville, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Jaana Männik

    Department of Physics and Astronomy, University of Tennessee, Knoxville, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Jaan Männik

    Department of Physics and Astronomy, University of Tennessee, Knoxville, United States
    For correspondence
    jmannik@utk.edu
    Competing interests
    The authors declare that no competing interests exist.
  5. Ariel Amir

    Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, United States
    For correspondence
    arielamir@seas.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2611-0139

Funding

US-Israel BSF Research Grant (2017004)

  • Jaan Männik

National Institutes of Health (R01GM127413)

  • Jaan Männik

National Science Foundation (NSF CAREER 1752024)

  • Ariel Amir

National Science Foundation (NSF award 1806818)

  • Prathitha Kar

National Institutes of Health (NIH grant 103346)

  • Prathitha Kar

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Gordon J Berman, Emory University, United States

Version history

  1. Received: July 28, 2021
  2. Accepted: November 21, 2021
  3. Accepted Manuscript published: December 2, 2021 (version 1)
  4. Accepted Manuscript updated: December 8, 2021 (version 2)
  5. Version of Record published: January 4, 2022 (version 3)
  6. Version of Record updated: January 7, 2022 (version 4)

Copyright

© 2021, Kar et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,826
    Page views
  • 337
    Downloads
  • 6
    Citations

Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Prathitha Kar
  2. Sriram Tiruvadi-Krishnan
  3. Jaana Männik
  4. Jaan Männik
  5. Ariel Amir
(2021)
Distinguishing different modes of growth using single-cell data
eLife 10:e72565.
https://doi.org/10.7554/eLife.72565

Share this article

https://doi.org/10.7554/eLife.72565

Further reading

    1. Microbiology and Infectious Disease
    Nguyen Thi Khanh Nhu, Minh-Duy Phan ... Mark A Schembri
    Research Article

    Neonatal meningitis is a devastating disease associated with high mortality and neurological sequelae. Escherichia coli is the second most common cause of neonatal meningitis in full-term infants (herein NMEC) and the most common cause of meningitis in preterm neonates. Here, we investigated the genomic relatedness of a collection of 58 NMEC isolates spanning 1974–2020 and isolated from seven different geographic regions. We show NMEC are comprised of diverse sequence types (STs), with ST95 (34.5%) and ST1193 (15.5%) the most common. No single virulence gene profile was conserved in all isolates; however, genes encoding fimbrial adhesins, iron acquisition systems, the K1 capsule, and O antigen types O18, O75, and O2 were most prevalent. Antibiotic resistance genes occurred infrequently in our collection. We also monitored the infection dynamics in three patients that suffered recrudescent invasive infection caused by the original infecting isolate despite appropriate antibiotic treatment based on antibiogram profile and resistance genotype. These patients exhibited severe gut dysbiosis. In one patient, the causative NMEC isolate was also detected in the fecal flora at the time of the second infection episode and after treatment. Thus, although antibiotics are the standard of care for NMEC treatment, our data suggest that failure to eliminate the causative NMEC that resides intestinally can lead to the existence of a refractory reservoir that may seed recrudescent infection.

    1. Microbiology and Infectious Disease
    Swati Jain, Gherman Uritskiy ... Venigalla B Rao
    Research Article

    A productive HIV-1 infection in humans is often established by transmission and propagation of a single transmitted/founder (T/F) virus, which then evolves into a complex mixture of variants during the lifetime of infection. An effective HIV-1 vaccine should elicit broad immune responses in order to block the entry of diverse T/F viruses. Currently, no such vaccine exists. An in-depth study of escape variants emerging under host immune pressure during very early stages of infection might provide insights into such a HIV-1 vaccine design. Here, in a rare longitudinal study involving HIV-1 infected individuals just days after infection in the absence of antiretroviral therapy, we discovered a remarkable genetic shift that resulted in near complete disappearance of the original T/F virus and appearance of a variant with H173Y mutation in the variable V2 domain of the HIV-1 envelope protein. This coincided with the disappearance of the first wave of strictly H173-specific antibodies and emergence of a second wave of Y173-specific antibodies with increased breadth. Structural analyses indicated conformational dynamism of the envelope protein which likely allowed selection of escape variants with a conformational switch in the V2 domain from an α-helix (H173) to a β-strand (Y173) and induction of broadly reactive antibody responses. This differential breadth due to a single mutational change was also recapitulated in a mouse model. Rationally designed combinatorial libraries containing 54 conformational variants of V2 domain around position 173 further demonstrated increased breadth of antibody responses elicited to diverse HIV-1 envelope proteins. These results offer new insights into designing broadly effective HIV-1 vaccines.