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REVIEW 4 major objections 5 minor 60 references

A mother-machine microfluidic device for non-adherent mammalian cells reveals the population growth strategies

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read T-cells tracked in traps split evenly, then partly size-correct

desk verdict A solid device paper whose biological claims need more rigorous statistics before they can carry the title. read the letter →

arxiv 2509.06113 v1 pith:H6ZYUU57 submitted 2025-09-07 q-bio.CB physics.bio-ph

Simone Scalise , Giovanna Peruzzi , Davide Caprini , Giorgio Gosti , Giancarlo Ruocco , Mattia Miotto This is my paper · ORCID
classification q-bio.CBphysics.bio-ph
keywords microfluidicsmothermachinesuspensioncellsTlineagetrackingcellsizecontrolsizermodeldivisionsymmetry
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper reports a microfluidic device that adapts the mother-machine design, narrow channels that trap one founder cell and let its progeny pile up in a line, to suspension cells such as human leukemia T-cells, which normally float and are hard to track. Continuous flow through open-ended channels keeps the cells supplied with nutrients, and imaging every minute for four days lets the authors reconstruct lineage trees across multiple generations. From those lineages they report three quantitative results: division is largely symmetric (a single Gaussian peak at an inherited fraction of 0.5 with $\sigma=0.05$), cells follow a partially compensating sizer-like strategy (a regression slope of $m=-0.24$ between added size and birth size), and division times peak at about 22 hours. The device also shows that channel inclination and medium flow change proliferation, with 45-degree channels giving the highest growth rates and no-flow conditions roughly doubling division times. If the measurements hold, the device gives suspension-cell biology a direct single-cell window that previously required indirect population-level inference.

What carries the argument

The central object is the open-ended mother-machine microchannel, a cell-sized trap (about 15 µm wide and 20 µm tall in this version) whose distal end connects to a drainage channel through a narrow constriction. The paper's design differs from closed-end bacterial mother machines by opening the distal end, which creates a slow unidirectional flow through each trap. That flow does two jobs: it carries cells in while preventing escape, and it continuously exchanges nutrients so cells deep in the channel are not starved. The main flow channel also has staggered pillar arrays that break up aggregates, and a dual-inlet layout separates buffer flow from cell loading. Around this geometry, the analysis machinery is a deep-learning segmentation pipeline that turns time-lapse images into cell masks and lineage trees, from which birth sizes, division sizes, and division times are extracted.

What would settle it

A reader could count every channel that contained a cell, record why each was excluded, and recompute the division-time distribution and the added-size versus birth-size regression with the excluded channels included. If the peak moves above 22 h or the slope moves toward zero, the reported sizer strategy is an artifact of channel selection.

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Extended reading notes

Core claim

On its own terms, the paper's central claim is that an open-ended, flow-through mother-machine chip can confine non-adherent mammalian cells well enough to follow hundreds of divisions and extract lineage statistics, and that human leukemia T-cells tracked this way divide symmetrically and control size through a partial sizer mechanism. The evidence is direct: the inherited-volume-fraction distribution is a single Gaussian centered at 0.5 with standard deviation 0.05, the regression of added size on birth size has slope $m=-0.24$ (between timer-like $m>0$ and pure sizer $m=-1$), and interdivision times peak near 22 h, consistent with culture doubling times. The paper also reports that 45-degree channel inclination gives the highest trapping probability (about 60%) and the fastest growth, and that stopping medium flow shifts division times to a bimodal distribution with peaks near 28 h and 46 h. These are presented as direct measurements of proliferation features that earlier work had inferred from flow cytometry.

Load-bearing premise

The load-bearing premise is that the channels manually selected as containing one cleanly dividing cell are representative of the proliferating population; if slow-dividing or unhealthy cells are preferentially excluded, the measured division-time peak and sizer slope would be biased.

Editorial extensions

If this is right

  • Direct lineage reconstruction becomes available for suspension cells, not just adherent cells or bacteria, so growth models can be tested on single cells rather than inferred from population snapshots.
  • The measured symmetric division and partial sizer slope provide a single-cell check on earlier cytometry-based estimates for this leukemia cell line.
  • Device geometry can be tuned: 45-degree channels combine higher trapping with faster growth, making them the preferred configuration for proliferation studies.
  • Continuous medium flow is a functional requirement, not a convenience; without it, division times widen and later-born cells divide much more slowly.
  • Because channels are sized to the cell, the same design should transfer to other suspension cell types by rescaling dimensions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A reanalysis that reports how many channels were rejected, and why, would test whether the 22 h peak and $m=-0.24$ slope are representative of the proliferating population or biased toward channels with fast, clean divisions.
  • The paper measures cell cross-sectional area, not volume; if a volume-resolved version produced a more negative slope, the underlying control could be closer to a pure sizer than the area-based number suggests.
  • The no-flow result implies that cells born under good conditions retain a memory of those conditions; this could be tested directly by switching flow on and off at defined times and watching division times in the same lineage.
  • The device could be adapted to primary T-cells or to drug perturbation, where lineage-level information about asymmetric division or cell-cycle arrest would be clinically relevant; that extension is natural but not demonstrated here.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The manuscript presents a mother-machine-like microfluidic device for non-adherent mammalian cells, specifically Jurkat T-cells. The device uses open-ended trapping channels, dual-inlet loading, and flow-through perfusion, with geometry optimized through computational fluid dynamics. The authors demonstrate lineage tracking over multiple generations and report three biological measurements: an inherited-fraction distribution fit by a single Gaussian with mu=0.5 and sigma=0.05 (interpreted as symmetric division), a regression slope of m=-0.24 between birth size and added size (interpreted as a sizer-like strategy), and a division-time distribution peaking at about 22 h. The paper is primarily an engineering and platform contribution, with the biological findings presented as direct single-cell measurements.

Significance. If the biological findings are robust, the platform is valuable because direct lineage tracking of suspension cells is technically challenging, and the device design is documented in sufficient detail to be reproduced. Strengths include the open-ended channel geometry, the dual-inlet loading scheme, the CFD-assisted optimization, and the direct time-lapse tracking rather than indirect flow-cytometry inference. However, the population-level biological claims currently rest on a manually curated subset of channels with no reported exclusion counts, sample sizes, or confidence intervals. The device engineering may be sound, but the growth-strategy conclusions are underdetermined by the evidence as presented.

major comments (4)
  1. [Section IV, Image segmentation; Fig. 4] The first analysis step is a manual selection of only channels where a single cell enters and divides, with explicit exclusions of multi-cell entries, non-dividing/senescent cells, death, overlapping daughters, and fused cells. Because every population-level quantity in Fig. 4—the Gaussian fit, the slope m=-0.24, and the division-time peak at 22 h—is computed only from this curated set, the reported numbers describe a potentially fast-proliferating subpopulation rather than the population growth strategy claimed in the title and abstract. No counts of included or excluded channels, no per-reason exclusion tallies, and no total number of tracked lineages are reported. Please provide the full channel census and a sensitivity analysis showing how the main statistics change under alternative inclusion rules.
  2. [Fig. 4] The paper reports a Gaussian fit with mu=0.5 and sigma=0.05 and a regression slope of m=-0.24 without any sample size, confidence interval, or goodness-of-fit statistic. The text states that a single timelapse permits following 'hundreds of division events', but the actual n for each panel is never stated. Without a confidence interval, the slope m=-0.24 cannot be distinguished from 0 (adder) or from -1 (pure sizer); the claim of a 'sizer-like' strategy is therefore not statistically grounded. Please provide n, standard errors, p-values, and fit diagnostics for every panel in Fig. 4, including the growth-rate distributions in Fig. 5.
  3. [Abstract and Section II.D] The abstract states that cells exhibit 'a slightly asymmetric volume division', but Section II.D reports that the fit returned a single Gaussian with mu=0.5 and sigma=0.05, explicitly 'consistent with symmetric divisions'. A single Gaussian centered at 0.5 is symmetric, so the abstract overstates the evidence. Please reconcile the abstract with the reported fit, or provide a separate measure of asymmetry (e.g., the two-Gaussian fit mentioned in the text) that supports the word 'asymmetric'.
  4. [Section II.D and Fig. 4b] The regression slope m=-0.24 is presented as the key evidence for a sizer-like strategy, but the analysis uses area at mid-height rather than volume, and the text acknowledges that area and volume are nonlinearly related. The line is fit to normalized areas without reporting the regression method, the uncertainty on the slope, the scatter around the line, or the number of cells. Please report the correlation coefficient, the standard error of m, and the results of testing against the null hypothesis m=0, so that the reader can assess the strength of the size-control claim.
minor comments (5)
  1. [Throughout] There are several typographical errors: 'fatc' should be 'fact', 'proceeeded' should be 'proceeded', and 'Figure b4' should be 'Figure 1b4'.
  2. [Fig. 3c caption] The caption says 'three of the six distinct lineages identified', but the total number of lineages in the full experiment is never defined. Please clarify what 'six' refers to and how the displayed branches were chosen.
  3. [Section II.D] Please state the exact number of division events, cell cycles, and channels used for each panel of Fig. 4, rather than the qualitative 'hundreds of division events'.
  4. [Fig. 5b] The claim that cells in 45-degree channels have the highest growth rates is made without sample sizes or a statistical test. Please add the number of cells per condition and a significance test for the differences among inclinations.
  5. [Data Availability] For a quantitative single-cell study, 'available from the corresponding author upon reasonable request' is not ideal. Consider depositing the segmentation masks, lineage tables, and analysis code in a public repository to enable reproduction and reuse.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the central lineage measurements are direct and self-contained; the same-group citations serve only as post-hoc corroboration.

full rationale

The paper's main biological results, including the symmetric inherited-fraction Gaussian (mu=0.5, sigma=0.05), the birth-size versus added-size regression slope m=-0.24, and the division-time peak near 22 h, are produced by direct image segmentation and lineage reconstruction from timelapse microscopy, not by fitting a model whose inputs already contain these outcomes. The geometric relation Delta-A = 2^(2/3) - 1 = 0.587 is a parameter-free calculation from the assumption V_d = 2 V_b, and it is not used to fit the measured slope or to generate the reported distributions. Citations to refs [5], [47], [48], and [49] are invoked as comparisons or methodological precedents after the measurements are made; even though several are from the same group, the present experimental values do not depend on those papers for their derivation, so this is corroboration rather than load-bearing circularity. The manual channel-selection step described in Section IV could bias the curated subpopulation, and the abstract's phrase 'slightly asymmetric volume division' conflicts with the reported single symmetric Gaussian, but these are issues of representativeness and internal consistency, not circularity. The derivation chain is therefore self-contained with respect to the measured quantities.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The quantitative biological results rest on measurement conventions (birth size 40 min after cytokinesis, division size 60 min before), two descriptive fits (Gaussian with mu=0.5, sigma=0.05 and regression slope -0.24), and the assumptions that mid-height area proxies volume, that volume doubles at division for the geometric comparison, and that manually selected channels are representative. No new physical entities are introduced.

free parameters (4)
  • Birth size reference time = 40 min after cytokinesis
    Chosen by hand as a consistent reference point for daughter cell birth size; affects all size-derived quantities including the sizer slope.
  • Division size reference time = 60 min before cytokinesis
    Chosen by hand to estimate division size; affects the added-size calculation and the regression slope.
  • Inherited fraction Gaussian parameters = mu = 0.5, sigma = 0.05
    Fitted to the observed distribution of daughter size fractions; used to claim symmetric division.
  • Sizer regression slope m = -0.24
    Fitted by linear regression of normalized added size on normalized birth size; used to claim sizer-like strategy.
assumptions (4)
  • domain assumption Cell area at mid-height is a faithful proxy for cell size/volume.
    All size measurements are 2D areas from segmentation; the authors compare to volume expectations using spherical-cell assumption (Section II.D).
  • domain assumption Volume doubles at division, V_d = 2 V_b, for the geometric comparison.
    Used to derive the expected area addition delta A = 0.587 in Section II.D; if volume doubling does not hold, the comparison to theory shifts.
  • domain assumption The slope of the birth-size vs added-size regression under exponential growth maps to sizer/adder/timer models.
    The interpretation of m=-0.24 as sizer-like relies on the standard linear-regression classification (Section II.D).
  • domain assumption Cellpose segmentation masks and manual channel selection yield unbiased area measurements.
    The analysis depends on Cellpose accuracy and on pre-selecting only channels with clean single-cell divisions (Section IV, Image segmentation).

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Cite this review

Pith. "Pith review of A mother-machine microfluidic device for non-adherent mammalian cells reveals the population growth strategies." pith.science (2026). https://pith.science/paper/H6ZYUU57

@misc{pith2026250906113,
  author       = {Pith},
  title        = {Pith review of: A mother-machine microfluidic device for non-adherent mammalian cells reveals the population growth strategies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H6ZYUU57}},
  note         = {Machine review of arXiv:2509.06113}
}
read the original abstract

We develop a mother machine-like microfluidic device specifically designed to track the proliferation of T-cells via live-cell microscopy. Although numerous microfluidic setups have been developed to study cell proliferation at the single-cell level, most of them are optimized for use on adherent cells. Here, we present a device to track the proliferation of suspension cells, featuring an array of microchannels that trap cells, easing their monitoring while allowing for controlled growth conditions. Each microchannel, whose geometry has been optimized through computational fluid dynamics simulations, allows a single cell to enter and proliferate while maintaining a continuous flow of nutrients, ensuring long-term monitoring over multiple generations. We show the advantages of this system in characterizing the proliferation of human leukemia T-cells. In particular, we follow the growth and division over multiple generations, finding that cells exhibit a slightly asymmetric volume division where deviations in the size are compensated by a size-like division strategy. Overall, our device design can be easily adapted and used to study different cell types and sizes while maintaining the same high trapping efficiency.

Figures

Figures reproduced from arXiv: 2509.06113 by the authors.

Figure 1
Figure 1. FIG. 1 [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2 [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3 [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: FIG. 4 [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5 [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

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Reference graph

Works this paper leans on

60 extracted references · 57 canonical work pages

  1. [1]

    Cell-size main- tenance: universal strategy revealed.Trends in microbi- ology, 23(1):4–6, 2015

    Suckjoon Jun and Sattar Taheri-Araghi. Cell-size main- tenance: universal strategy revealed.Trends in microbi- ology, 23(1):4–6, 2015

    work page 2015

  2. [2]

    Phenotypic diversity, population growth, and information in fluctuating envi- ronments.Science, 309(5743):2075–2078, 2005

    Edo Kussell and Stanislas Leibler. Phenotypic diversity, population growth, and information in fluctuating envi- ronments.Science, 309(5743):2075–2078, 2005. 11

    work page 2005

  3. [3]

    Exploration-exploitation tradeoffs dictate the optimal distributions of phenotypes for populations sub- ject to fitness fluctuations.Physical Review E, 99(1), January 2019

    Andrea De Martino, Thomas Gueudr´ e, and Mattia Miotto. Exploration-exploitation tradeoffs dictate the optimal distributions of phenotypes for populations sub- ject to fitness fluctuations.Physical Review E, 99(1), January 2019

    work page 2019

  4. [4]

    The contribution of asymmetric cell division to pheno- typic heterogeneity in cancer.Journal of Cell Science, 137(5):jcs261400, 2024

    Julieti Huch Buss, Karine Rech Begnini, and Guido Lenz. The contribution of asymmetric cell division to pheno- typic heterogeneity in cancer.Journal of Cell Science, 137(5):jcs261400, 2024

    work page 2024

  5. [5]

    A size-dependent division strategy accounts for leukemia cell size heterogeneity.Communications Physics, 7(1), July 2024

    Mattia Miotto, Simone Scalise, Marco Leonetti, Gian- carlo Ruocco, Giovanna Peruzzi, and Giorgio Gosti. A size-dependent division strategy accounts for leukemia cell size heterogeneity.Communications Physics, 7(1), July 2024

    work page 2024

  6. [6]

    Probing leukemia cells behavior under starvation.arXiv, 2024

    Simone Scalise, Giorgio Gosti, Giancarlo Ruocco, Gio- vanna Peruzzi, and Mattia Miotto. Probing leukemia cells behavior under starvation.arXiv, 2024

    work page 2024

  7. [7]

    Dynamics of single-cell gene expression.Molecular systems biology, 2(1):64, 2006

    Diane Longo and Jeff Hasty. Dynamics of single-cell gene expression.Molecular systems biology, 2(1):64, 2006

    work page 2006

  8. [8]

    Retracing the in vivo haematopoietic tree using single-cell methods.FEBS let- ters, 590(22):4068–4083, 2016

    Le¨ ıla Peri´ e and Ken R Duffy. Retracing the in vivo haematopoietic tree using single-cell methods.FEBS let- ters, 590(22):4068–4083, 2016

    work page 2016

Show all 60 references
  1. [9]

    Advan- tages and challenges of microfluidic cell culture in poly- dimethylsiloxane devices.Biosensors and Bioelectronics, 63:218–231, 2015

    Skarphedinn Halldorsson, Edinson Lucumi, Rafael G´ omez-Sj¨ oberg, and Ronan MT Fleming. Advan- tages and challenges of microfluidic cell culture in poly- dimethylsiloxane devices.Biosensors and Bioelectronics, 63:218–231, 2015

    2015

  2. [10]

    Easy and low-cost stable positioning of suspension cells during live-cell imaging.Journal of biological methods, 4(4):e80, 2017

    Daniel Ivanusic, Kazimierz Madela, and Joachim Denner. Easy and low-cost stable positioning of suspension cells during live-cell imaging.Journal of biological methods, 4(4):e80, 2017

    2017

  3. [11]

    Classification of cell types using a microfluidic device for mechanical and electrical measurement on single cells.Lab on a Chip, 11(18):3174–3181, 2011

    Jian Chen, Yi Zheng, Qingyuan Tan, Ehsan Shojaei- Baghini, Yan Liang Zhang, Jason Li, Preethy Prasad, Lidan You, Xiao Yu Wu, and Yu Sun. Classification of cell types using a microfluidic device for mechanical and electrical measurement on single cells.Lab on a Chip, 11(18):317...

    2011

  4. [12]

    Jhih-Lin Hong, Kung-Chieh Lan, and Ling-Sheng Jang. Electrical characteristics analysis of various cancer cells using a microfluidic device based on single-cell impedance measurement.Sensors and Actuators B: Chemical, 173:927–934, 2012

    2012

  5. [13]

    Johnny Lam, Ross A Marklein, Jose A Jimenez-Torres, David J Beebe, Steven R Bauer, and Kyung E Sung. Adaptation of a simple microfluidic platform for high- dimensional quantitative morphological analysis of hu- man mesenchymal stromal cells on polystyrene-based substrates.SLAS...

    2017

  6. [14]

    Via- bility analysis and apoptosis induction of breast cancer cells in a microfluidic device: effect of cytostatic drugs

    Job Komen, Floor Wolbers, Henk R Franke, Helene An- dersson, Istvan Vermes, and Albert van den Berg. Via- bility analysis and apoptosis induction of breast cancer cells in a microfluidic device: effect of cytostatic drugs. Biomedical microdevices, 10(5):727–737, 2008

    2008

  7. [15]

    Mi- crofluidic device for single-cell analysis.Analytical chem- istry, 75(14):3581–3586, 2003

    Aaron R Wheeler, William R Throndset, Rebecca J Whe- lan, Andrew M Leach, Richard N Zare, Yish Hann Liao, Kevin Farrell, Ian D Manger, and Antoine Daridon. Mi- crofluidic device for single-cell analysis.Analytical chem- istry, 75(14):3581–3586, 2003

    2003

  8. [16]

    Nutrichip: nutrition analysis meets mi- crofluidics.Lab on a Chip, 13(2):196–203, 2013

    Qasem Ramadan, Hamideh Jafarpoorchekab, Chaobo Huang, Paolo Silacci, Sandro Carrara, G¨ ozen Kokl¨ u, Julien Ghaye, Jeremy Ramsden, Christine Ruffert, Guy Vergeres, et al. Nutrichip: nutrition analysis meets mi- crofluidics.Lab on a Chip, 13(2):196–203, 2013

    2013

  9. [17]

    Quantitative study of the dynamic tumor–endothelial cell interactions through an integrated microfluidic coculture system.Analytical chemistry, 84(4):2088–2093, 2012

    Chunhong Zheng, Liang Zhao, Gui’e Chen, Ying Zhou, Yuhong Pang, and Yanyi Huang. Quantitative study of the dynamic tumor–endothelial cell interactions through an integrated microfluidic coculture system.Analytical chemistry, 84(4):2088–2093, 2012

    2012

  10. [18]

    Imaging single-cell signaling dynamics with a deterministic high-density single-cell trap array.Ana- lytical chemistry, 83(18):7044–7052, 2011

    Kwanghun Chung, Catherine A Rivet, Melissa L Kemp, and Hang Lu. Imaging single-cell signaling dynamics with a deterministic high-density single-cell trap array.Ana- lytical chemistry, 83(18):7044–7052, 2011

    2011

  11. [19]

    Cell stimulus and lysis in a microfluidic device with segmented gas- liquid flow.An- alytical chemistry, 77(11):3629–3636, 2005

    Jamil El-Ali, Suzanne Gaudet, Axel G¨ unther, Peter K Sorger, and Klavs F Jensen. Cell stimulus and lysis in a microfluidic device with segmented gas- liquid flow.An- alytical chemistry, 77(11):3629–3636, 2005

    2005

  12. [20]

    A microfluidic device enabling deterministic single cell trapping and release.Lab on a Chip, 21(13):2486– 2494, 2021

    Huichao Chai, Yongxiang Feng, Fei Liang, and Wenhui Wang. A microfluidic device enabling deterministic single cell trapping and release.Lab on a Chip, 21(13):2486– 2494, 2021

    2021

  13. [21]

    A microfluidic device enabling high-efficiency single cell trapping.Biomicrofluidics, 9(1), 2015

    Di Jin, Bin Deng, JX Li, W Cai, L Tu, J Chen, Q Wu, and WH Wang. A microfluidic device enabling high-efficiency single cell trapping.Biomicrofluidics, 9(1), 2015

    2015

  14. [22]

    A microfluidic device for hydrodynamic trapping and manipulation platform of a single biological cell.Applied Sciences, 6(2):40, 2016

    Amelia Ahmad Khalili, Mohd Ridzuan Ahmad, Masaru Takeuchi, Masahiro Nakajima, Yasuhisa Hasegawa, and Razauden Mohamed Zulkifli. A microfluidic device for hydrodynamic trapping and manipulation platform of a single biological cell.Applied Sciences, 6(2):40, 2016

    2016

  15. [23]

    Dynamic single cell culture array.Lab on a Chip, 6(11):1445–1449, 2006

    Dino Di Carlo, Liz Y Wu, and Luke P Lee. Dynamic single cell culture array.Lab on a Chip, 6(11):1445–1449, 2006

    2006

  16. [24]

    A microfluidic chip for screening individual cancer cells via eavesdropping on autophagy- inducing crosstalk in the stroma niche.Scientific reports, 7(1):2050, 2017

    Hacer Ezgi Karakas, Junyoung Kim, Juhee Park, Jung Min Oh, Yongjun Choi, Devrim Gozuacik, and Yoon-Kyoung Cho. A microfluidic chip for screening individual cancer cells via eavesdropping on autophagy- inducing crosstalk in the stroma niche.Scientific reports, 7(1):2050, 2017

    2017

  17. [25]

    A microfluidic dual-well device for high-throughput single-cell capture and culture.Lab on a Chip, 15(14):2928–2938, 2015

    Ching-Hui Lin, Yi-Hsing Hsiao, Hao-Chen Chang, Chuan-Feng Yeh, Cheng-Kun He, Eric M Salm, Chihchen Chen, Ming Chiu, and Chia-Hsien Hsu. A microfluidic dual-well device for high-throughput single-cell capture and culture.Lab on a Chip, 15(14):2928–2938, 2015

    2015

  18. [26]

    Tracking bacterial lineages in complex and dynamic environments with applications for growth control and persistence.Nature microbiology, 6(6):783–791, 2021

    Somenath Bakshi, Emanuele Leoncini, Charles Baker, Silvia J Ca˜ nas-Duarte, Burak Okumus, and Johan Pauls- son. Tracking bacterial lineages in complex and dynamic environments with applications for growth control and persistence.Nature microbiology, 6(6):783–791, 2021

    2021

  19. [27]

    Peng Sun, Yang Liu, Jun Sha, Zhiyun Zhang, Qin Tu, Peng Chen, and Jinyi Wang. High-throughput microflu- idic system for long-term bacterial colony monitoring and antibiotic testing in zero-flow environments.Biosensors and Bioelectronics, 26(5):1993–1999, 2011

    1993

  20. [28]

    Quantitative modelling of nutrient- limited growth of bacterial colonies in microfluidic cultivation.Journal of the Royal Society Interface, 15(139):20170713, 2018

    Raphael Hornung, Alexander Gr¨ unberger, Christoph Westerwalbesloh, Dietrich Kohlheyer, Gerhard Gomp- per, and Jens Elgeti. Quantitative modelling of nutrient- limited growth of bacterial colonies in microfluidic cultivation.Journal of the Royal Society Interface, 15(139):2017...

    2018

  21. [29]

    High-throughput analysis of yeast replica- tive aging using a microfluidic system.Proceedings of the National Academy of Sciences, 112(30):9364–9369, 2015

    Myeong Chan Jo, Wei Liu, Liang Gu, Weiwei Dang, and Lidong Qin. High-throughput analysis of yeast replica- tive aging using a microfluidic system.Proceedings of the National Academy of Sciences, 112(30):9364–9369, 2015

    2015

  22. [30]

    Yeast repli- cator: a high-throughput multiplexed microfluidics plat- form for automated measurements of single-cell aging

    Ping Liu, Thomas Z Young, and Murat Acar. Yeast repli- cator: a high-throughput multiplexed microfluidics plat- form for automated measurements of single-cell aging. Cell reports, 13(3):634–644, 2015

    2015

  23. [31]

    A microfluidic system for studying ageing and dynamic single-cell responses in budding yeast.PloS one, 9(6):e100042, 2014

    Matthew M Crane, Ivan BN Clark, Elco Bakker, Stew- 12 art Smith, and Peter S Swain. A microfluidic system for studying ageing and dynamic single-cell responses in budding yeast.PloS one, 9(6):e100042, 2014

    2014

  24. [32]

    Vacuum- assisted cell loading enables shear-free mammalian mi- crofluidic culture.Lab on a chip, 12(22):4732–4737, 2012

    Martin Kolnik, Lev S Tsimring, and Jeff Hasty. Vacuum- assisted cell loading enables shear-free mammalian mi- crofluidic culture.Lab on a chip, 12(22):4732–4737, 2012

    2012

  25. [33]

    Self-loading and cell culture in one layer microfluidic devices.Biomedical microdevices, 11(3):679–684, 2009

    Li Wang, Xiao-Fang Ni, Chun-Xiong Luo, Zhi-Ling Zhang, Dai-Wen Pang, and Yong Chen. Self-loading and cell culture in one layer microfluidic devices.Biomedical microdevices, 11(3):679–684, 2009

    2009

  26. [34]

    Design of a 2d no- flow chamber to monitor hematopoietic stem cells.Lab on a Chip, 15(1):77–85, 2015

    Th´ eo Cambier, Thibault Honegger, Val´ erie Vanneaux, Jean Berthier, David Peyrade, Laurent Blanchoin, Jerome Larghero, and Manuel Th´ ery. Design of a 2d no- flow chamber to monitor hematopoietic stem cells.Lab on a Chip, 15(1):77–85, 2015

    2015

  27. [35]

    A new method for study- ing gradient-induced neutrophil desensitization based on an open microfluidic chamber.Lab on a Chip, 10(1):116– 122, 2010

    Thomas M Keenan, Charles W Frevert, Aileen Wu, Venus Wong, and Albert Folch. A new method for study- ing gradient-induced neutrophil desensitization based on an open microfluidic chamber.Lab on a Chip, 10(1):116– 122, 2010

    2010

  28. [36]

    Robust growth of escherichia coli.Current biology, 20(12):1099–1103, 2010

    Ping Wang, Lydia Robert, James Pelletier, Wei Lien Dang, Francois Taddei, Andrew Wright, and Suckjoon Jun. Robust growth of escherichia coli.Current biology, 20(12):1099–1103, 2010

    2010

  29. [37]

    Mechanis- tic origin of cell-size control and homeostasis in bacteria

    Fangwei Si, Guillaume Le Treut, John T Sauls, Stephen Vadia, Petra Anne Levin, and Suckjoon Jun. Mechanis- tic origin of cell-size control and homeostasis in bacteria. Current Biology, 29(11):1760–1770, 2019

    2019

  30. [38]

    Tools and methods for high-throughput single-cell imaging with the mother machine.Elife, 12:RP88463, 2024

    Ryan Thiermann, Michael Sandler, Gursharan Ahir, John T Sauls, Jeremy Schroeder, Steven Brown, Guil- laume Le Treut, Fangwei Si, Dongyang Li, Jue D Wang, et al. Tools and methods for high-throughput single-cell imaging with the mother machine.Elife, 12:RP88463, 2024

    2024

  31. [39]

    Single-cell physi- ology.Annual review of biophysics, 44(1):123–142, 2015

    Sattar Taheri-Araghi, Steven D Brown, John T Sauls, Dustin B McIntosh, and Suckjoon Jun. Single-cell physi- ology.Annual review of biophysics, 44(1):123–142, 2015

    2015

  32. [40]

    Analysis of factors limiting bacterial growth in pdms mother machine devices.Fron- tiers in microbiology, 9:871, 2018

    Da Yang, Anna D Jennings, Evalynn Borrego, Scott T Retterer, and Jaan M¨ annik. Analysis of factors limiting bacterial growth in pdms mother machine devices.Fron- tiers in microbiology, 9:871, 2018

    2018

  33. [41]

    Memory and modularity in cell-fate decision making.Nature, 503(7477):481–486, 2013

    Thomas M Norman, Nathan D Lord, Johan Paulsson, and Richard Losick. Memory and modularity in cell-fate decision making.Nature, 503(7477):481–486, 2013

    2013

  34. [42]

    Effect of a dual inlet channel on cell loading in microfluidics.Biomi- crofluidics, 8(6), 2014

    Hoyoung Yun, Kisoo Kim, and Won Gu Lee. Effect of a dual inlet channel on cell loading in microfluidics.Biomi- crofluidics, 8(6), 2014

    2014

  35. [43]

    Comparison of chip inlet geometry in microfluidic devices for cell studies.Molecules, 21(6):778, 2016

    Yung-Shin Sun. Comparison of chip inlet geometry in microfluidic devices for cell studies.Molecules, 21(6):778, 2016

    2016

  36. [44]

    Uniform cell distribution achieved by using cell deformation in a mi- cropillar array.Micromachines, 6(4):409–422, 2015

    Maho Kaminaga, Tadashi Ishida, Tetsuya Kadonosono, Shinae Kizaka-Kondoh, and Toru Omata. Uniform cell distribution achieved by using cell deformation in a mi- cropillar array.Micromachines, 6(4):409–422, 2015

    2015

  37. [45]

    Size control in mammalian cells involves modulation of both growth rate and cell cycle duration

    Clotilde Cadart, Sylvain Monnier, Jacopo Grilli, Pablo J S´ aez, Nishit Srivastava, Rafaele Attia, Emmanuel Ter- riac, Buzz Baum, Marco Cosentino-Lagomarsino, and Matthieu Piel. Size control in mammalian cells involves modulation of both growth rate and cell cycle duration. Na...

    2018

  38. [46]

    Optical volume and mass measurements show that mammalian cells swell during mitosis.Journal of Cell Biology, 211(4):765–774, 2015

    Ewa Zlotek-Zlotkiewicz, Sylvain Monnier, Giovanni Cap- pello, Mael Le Berre, and Matthieu Piel. Optical volume and mass measurements show that mammalian cells swell during mitosis.Journal of Cell Biology, 211(4):765–774, 2015

    2015

  39. [47]

    Ro- bust assessment of asymmetric division in colon cancer cells.eLife, March 2025

    Domenico Caudo, Chiara Giannattasio, Simone Scalise, Valeria de Turris, Fabio Giavazzi, Giancarlo Ruocco, Giorgio Gosti, Giovanna Peruzzi, and Mattia Miotto. Ro- bust assessment of asymmetric division in colon cancer cells.eLife, March 2025

    2025

  40. [48]

    Asymmetric bino- mial statistics explains organelle partitioning variance in cancer cell proliferation.Communications Physics, 4(1), August 2021

    Giovanna Peruzzi, Mattia Miotto, Roberta Maggio, Gi- ancarlo Ruocco, and Giorgio Gosti. Asymmetric bino- mial statistics explains organelle partitioning variance in cancer cell proliferation.Communications Physics, 4(1), August 2021

    2021

  41. [49]

    Quantifying the organisation in can- cer cell partitioning noise.Philosophical Magazine, page 1–14, May 2025

    Mattia Miotto, Giovanna Peruzzi, Giorgio Gosti, and Gi- ancarlo Ruocco. Quantifying the organisation in can- cer cell partitioning noise.Philosophical Magazine, page 1–14, May 2025

    2025

  42. [50]

    Cell-size control and homeostasis in bacteria.Current biology, 25(3):385–391, 2015

    Sattar Taheri-Araghi, Serena Bradde, John T Sauls, Nor- bert S Hill, Petra Anne Levin, Johan Paulsson, Mas- simo Vergassola, and Suckjoon Jun. Cell-size control and homeostasis in bacteria.Current biology, 25(3):385–391, 2015

    2015

  43. [51]

    Controlling cell size through sizer mechanisms.Current Opinion in Systems Biology, 5:86–92, 2017

    Giuseppe Facchetti, Fred Chang, and Martin Howard. Controlling cell size through sizer mechanisms.Current Opinion in Systems Biology, 5:86–92, 2017

    2017

  44. [52]

    Cell size regulation in bacteria.Physical review letters, 112(20):208102, 2014

    Ariel Amir. Cell size regulation in bacteria.Physical review letters, 112(20):208102, 2014

    2014

  45. [53]

    Unification of cell division control strategies through continuous rate models.Physical Review E, 101(2):022401, 2020

    C´ esar Nieto, Juan Arias-Castro, Carlos S´ anchez, C´ esar Vargas-Garc´ ıa, and Juan Manuel Pedraza. Unification of cell division control strategies through continuous rate models.Physical Review E, 101(2):022401, 2020

    2020

  46. [54]

    A simple molecular mechanism explains multiple patterns of cell-size regulation.PloS one, 12(8):e0182633, 2017

    Morgan Delarue, Daniel Weissman, and Oskar Hal- latschek. A simple molecular mechanism explains multiple patterns of cell-size regulation.PloS one, 12(8):e0182633, 2017

    2017

  47. [55]

    Initial cell density encodes proliferative potential in cancer cell populations.Scien- tific Reports, 11(1), March 2021

    Chiara Enrico Bena, Marco Del Giudice, Alice Grob, Thomas Gueudr´ e, Mattia Miotto, Dimitra Gialama, Matteo Osella, Emilia Turco, Francesca Ceroni, Andrea De Martino, and Carla Bosia. Initial cell density encodes proliferative potential in cancer cell populations.Scien- tific ...

    2021

  48. [56]

    Long-term single cell analysis of s

    Jean-Bernard Nobs and Sebastian J Maerkl. Long-term single cell analysis of s. pombe on a microfluidic mi- crochemostat array.PloS one, 9(4):e93466, 2014

    2014

  49. [57]

    How to perform a microfluidic cultivation experiment—a guideline to success.Biosensors, 11(12):485, 2021

    Sarah T¨ auber, Julian Schmitz, Luisa Bl¨ obaum, Niklas Fante, Heiko Steinhoff, and Alexander Gr¨ unberger. How to perform a microfluidic cultivation experiment—a guideline to success.Biosensors, 11(12):485, 2021

    2021

  50. [58]

    Development and application of a cultiva- tion platform for mammalian suspension cell lines with single-cell resolution.Biotechnology and bioengineering, 118(2):992–1005, 2021

    Julian Schmitz, Sarah T¨ auber, Christoph Wester- walbesloh, Eric von Lieres, Thomas Noll, and Alexander Gr¨ unberger. Development and application of a cultiva- tion platform for mammalian suspension cell lines with single-cell resolution.Biotechnology and bioengineering, 118(...

    2021

  51. [59]

    Cellpose: a generalist algorithm for cellular segmentation.Nature methods, 18(1):100–106, 2021

    Carsen Stringer, Tim Wang, Michalis Michaelos, and Marius Pachitariu. Cellpose: a generalist algorithm for cellular segmentation.Nature methods, 18(1):100–106, 2021

    2021

  52. [60]

    Cellpose 2.0: how to train your own model.Nature methods, 19(12):1634–1641, 2022

    Marius Pachitariu and Carsen Stringer. Cellpose 2.0: how to train your own model.Nature methods, 19(12):1634–1641, 2022

    2022

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.