An image-guided microfluidic system for single-cell lineage tracking

PLOS ONE RESEARCH ARTICLE An image-guided microfluidic system for single-cell lineage tracking Mahmut Aslan Kamil1☯, Camille Fourneaux2☯, Alperen Yilmaz3, Stavrakis Stavros1, Romuald Parmentier4, Andras Paldi ID4, Sandrine Gonin-Giraud2, Andrew J. deMello1, Olivier Gandrillon ID2,5* 1 Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Zürich, Switzerland, 2 Laboratory of Biology and Modelling of the Cell, Université de Lyon, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard, Lyon, France, 3 Faculty of Medicine, Koç University, Istanbul, Turkey, 4 Ecole Pratique des Hautes Etudes, St-Antoine Research Center, Inserm U938, PSL Research University, Paris, France, 5 Inria, France a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 ☯ These authors contributed equally to this work. * Abstract OPEN ACCESS Citation: Aslan Kamil M, Fourneaux C, Yilmaz A, Stavros S, Parmentier R, Paldi A, et al. (2023) An image-guided microfluidic system for single-cell lineage tracking. PLoS ONE 18(8): e0288655. https://doi.org/10.1371/journal.pone.0288655 Editor: Hon Fai Chan, Chinese University of Hong Kong, HONG KONG Received: April 4, 2023 Accepted: June 30, 2023 Published: August 1, 2023 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0288655 Copyright: © 2023 Aslan Kamil et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The datasets supporting the conclusions of this article are available at https://www.ncbi.nlm.nih.gov/ bioproject/PRJNA882740. The R scripts are Cell lineage tracking is a long-standing and unresolved problem in biology. Microfluidic technologies have the potential to address this problem, by virtue of their ability to manipulate and process single-cells in a rapid, controllable and efficient manner. Indeed, when coupled with traditional imaging approaches, microfluidic systems allow the experimentalist to follow single-cell divisions over time. Herein, we present a valve-based microfluidic system able to probe the decision-making processes of single-cells, by tracking their lineage over multiple generations. The system operates by trapping single-cells within growth chambers, allowing the trapped cells to grow and divide, isolating sister cells after a user-defined number of divisions and finally extracting them for downstream transcriptome analysis. The platform incorporates multiple cell manipulation operations, image processing-based automation for cell loading and growth monitoring, reagent addition and device washing. To demonstrate the efficacy of the microfluidic workflow, 6C2 (chicken erythroleukemia) and T2EC (primary chicken erythrocytic progenitors) cells are tracked inside the microfluidic device over two generations, with a cell viability rate in excess of 90%. Sister cells are successfully isolated after division and extracted within a 500 nL volume, which was demonstrated to be compatible with downstream single-cell RNA sequencing analysis. Introduction One of the biggest challenges in quantitative biology is to better understand the decision-making process of cells. Over the past 20 years, a change in the scale of investigation from cell populations to the single-cell level has already brought numerous insights of such processes [1–3]. The primary benefit of performing experiments at the single-cell level is the ability to reveal the underlying transcriptional heterogeneity of both normal and pathological cells [4, 5]. Furthermore, single-cell studies have already provided evidence that gene expression variability is a property of cell fate decision making [3, 6]. PLOS ONE | https://doi.org/10.1371/journal.pone.0288655 August 1, 2023 1 / 14 PLOS ONE available on the Git repository at https://gitbio.enslyon.fr/cfournea/sincity. Funding: This work was supported by funding from the French agency ANR (SinCity; ANR-17CE12-0031). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. An image-guided microfluidic system for single-cell lineage tracking Cellular differentiation is the process by which any pre-committed cell acquires its identity, and can be viewed as a dynamic process wired by the underlying gene regulatory network (GRN). Cells can be thought of as "moving particles" within a landscape, with the cell state space shaped by the GRN state [7]. According to this view, within this landscape, points of stability are referred as “steady states” and can be represented by attraction wells. Cells can escape their self-renewing steady state through a rise in gene expression variability and then explore freely, to some extent, the landscape to finally reach a new state of equilibrium; the differentiated state [7]. Single-cell analysis of in vitro and in vivo differentiation models have confirmed that this cellular process is indeed characterized by a global rise in gene expression variability [8–12]. That said, the way that gene expression variability is established across cell generations is still poorly understood. Such a fundamental question is likely to be of critical importance as it seems to be a conserved phenomenon across both biological systems and species [13–16]. Indeed, at the organism scale, during differentiation, cells must maintain their lineage identity through mitosis and eventually reach their differentiation state. Based on recent studies, support for this state memory comes from the inheritance of mRNA levels from mother cells to daughter cells [13]. This transmission is, with high probability, supported by the inheritance of epigenetic modifications allowing the maintenance of gene-specific transcription levels over cell divisions [16, 17]. Recently, it has been noted that in some genes, in which expression is variable amongst an isogenic cell population, expression is correlated between genealogically related cells [13, 14]. For some of these “memory genes”, the correlation in expression may last for tens of generations. These data, gathered on self-renewing cells, imply a gene-specific transcriptional memory over several cell generations [13]. We recently developed experimental methods to recover related cells after one (first generation) and two (second generation) cell divisions, in order to investigate how cells reconcile the constraints of transcriptional memory and the rise in gene expression (...truncated)