Microscopic PhotoSelection (MiPS) of single cells in mother machine microfluidic devices

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   Review Commons

   Jun 16, 2026

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   Authors do not wish to provide a response at this time; please see attached Revision Plan.

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   Review Commons

   Jun 16, 2026

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   Referee #3

   Evidence, reproducibility and clarity

   The work describes an optofluidic automation setup to optically inhibit and enrich selected bacterial populations in confined microchannels through negative selection using light stimulation. The work is well described and the manuscript is well constructed.

   major comment: The authors reported that methylene blue with 2uM incubation has superior performance than UV light. But it's also noted on line 152 there is an inhibition effect from the chemical affecting ~40% of the growth rate. It will be noteworthy what is the growth curve or at least the MIC of methylene blue used on the MG1655 E. coli by the authors.

   Minor:

   Figure 3A has …

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   Referee #3

   Evidence, reproducibility and clarity

   The work describes an optofluidic automation setup to optically inhibit and enrich selected bacterial populations in confined microchannels through negative selection using light stimulation. The work is well described and the manuscript is well constructed.

   major comment: The authors reported that methylene blue with 2uM incubation has superior performance than UV light. But it's also noted on line 152 there is an inhibition effect from the chemical affecting ~40% of the growth rate. It will be noteworthy what is the growth curve or at least the MIC of methylene blue used on the MG1655 E. coli by the authors.

   Minor:

   Figure 3A has examined the off-target growth rate effects. Statistics were made as shown in the subfigure. However, the details of the statistical inference seems to be missed in the materials and methods. Figure 4D highlights the novelty of the work to enrich mCherry E.coli population by selectively inhibiting GFP populations. However, this figure is lacking error bars which should be available given the population data. I would applaud the authors to describe the optical setup in good detail. However, since the throughput of the mother machine microfluidic device and the FOV throughput were discussed in the discussion. From Fig.S1 the mother machine device seems of special design. A more detailed description of the trench dimension, depth, and number of trenches on each device is warranted.

   Significance

   The optics part of the work is well described, however the materials and methods details of the biological and microfluidic part can be extended. Overall the system demonstrated the practical use of combining microfluidics for enrichment of microbial population as an novel alternative method, despite that the efficiency is currently subpar to conventional methods.

   But combining further with deep learning phenotype or growth rate monitoring, the technology represents a new path for phenotypic selection which is also novel that conventional methods cannot offer. The work will benefit readers in applied science seeking for new target enrichment based on optofluidics.

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3. 

   Review Commons

   Jun 16, 2026

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   Referee #2

   Evidence, reproducibility and clarity

   In this manuscript, the authors reported Microscopic PhotoSelection (MiPS), a closed-loop automated robotic platform designed to link time-resolved imaging with physical sample recovery in mother machine microfluidic devices. By pairing a standard mother machine layout with a custom DMD optical path, an LED array, and an optimized DeLTA deep-learning model, the system tracks dynamic single-cell phenotypes and isolates specific cells via automated, targeted phototoxicity, i.e. selection by elimination. This is a novel technical development that addresses a clear limitation of snapshot sorting methods like FACS or MACS when screening …

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   Referee #2

   Evidence, reproducibility and clarity

   In this manuscript, the authors reported Microscopic PhotoSelection (MiPS), a closed-loop automated robotic platform designed to link time-resolved imaging with physical sample recovery in mother machine microfluidic devices. By pairing a standard mother machine layout with a custom DMD optical path, an LED array, and an optimized DeLTA deep-learning model, the system tracks dynamic single-cell phenotypes and isolates specific cells via automated, targeted phototoxicity, i.e. selection by elimination. This is a novel technical development that addresses a clear limitation of snapshot sorting methods like FACS or MACS when screening for time-resolved, lineage-dependent traits. However, several methodological limitations and presentation errors must be addressed before publication.

   Major Comments

   1. Definition of 'Optimal' Dose (Figure 2D): The authors identify 8.0 W*cm-2 UV light for 300s as the optimal condition. However, this data point lies at the absolute boundary of the tested parameter space. In classical dose-response characterization, an optimum is defined by a local peak or a plateau followed by a decline in performance (typically due to rising off-target toxicity or scatter). Because the performance curve has not rolled over, this represents a boundary condition rather than a demonstrated mathematical optimum. The authors should either extend the parameter sweep to locate the true peak or soften their language to reflect that this is simply the highest performing condition tested.
   2. UV Exposure Time Gap: The exposure time sweep skips directly from 60s to 300s. While the closely spaced early timepoints are appropriate for capturing initial cell-death kinetics, the large gap to 300s leaves a significant engineering blind spot. Figure 3D demonstrates that off-target scattering damage scales linearly with cumulative light energy. If complete target cell arrest can be achieved at an intermediate exposure (e.g., 120s, 180s or 240s), operating the system at 300s unnecessarily subjects neighboring "surviving" cells to secondary global UV stress via device-wide scattering. An intermediate temporal sweep is recommended to optimize the selection window and properly balance target lethality with background library viability.
   3. Baseline Chemical Toxicity of Methylene Blue (MB): The photosensitizer workflow shows a clear improvement in contrast at lower power densities and exposure times. However, lines 151-153 note that the addition of 2 uM MB alone, even without light activation, stunts the baseline bacterial growth rate by ~40%. This is a major biological confounder. For applications like directed evolution or dynamic physiological screening, introducing a chemical stressor that nearly halves fitness imposes an unintended selective pressure. This baseline stress may activate pathways that mask or alter the phenotypes of interest. The authors must expand their discussion on how this baseline toxicity impacts multi-round iterative selections, and should ideally evaluate lower concentrations (e.g., 0.5uM or 1uM) or alternative photosensitizers to identify a more viable operational window.
   4. Negative Selection Framework and Search Space Scale: The MiPS platform relies entirely on negative selection by destroying unwanted variants. While effective for the demonstrated 1:1 binary proof-of-concept mixture, negative selection scales poorly when screening for rare variants within large libraries. For instance, isolating a single high-performer from a library of 105 cells requires the system to successfully target and kill 99,999 individual cells; any statistical leak or failure in killing efficiency directly leads to heavy contamination of the recovered sample. The Discussion section requires a quantitative evaluation of these search space constraints, outlining how they limit the system's utility compared to positive selection mechanisms (such as optical tweezers or droplet sorters) when scaling to rare mutations (<1 in 104).

   Minor and Typographical Comments

   1. Missing Figure 2F: On page 6, line 150, the text explicitly cites Figure 2F to justify the 5-fold reduction in exposure duration for the MB photosensitizer workflow. However, Figure 2 ends at panel E. The authors must either supply the missing panel or correct the text reference.
   2. Textual Corrections:

   a. Line 224: "At reach round of the simulation..." should read "At each round..."

   b. Line 285: "By observing cells over longer durations and averaging the measurements, resulting in a readout closer to the "true" selected phenotype." This is a grammatically incomplete sentence fragment. Please revise for proper syntax.

   c. Line 302: "...these advantages highight MiPS as an enabler..." Typo in "highight"; change to "highlight."

   Significance

   This study presents a significant methodological advance in single-cell analysis and microfluidics by integrating long-term live-cell imaging, automated image analysis, and phenotype-guided cell recovery into a closed-loop platform. Existing approaches such as FACS and MACS are largely limited to endpoint or snapshot measurements, whereas MiPS enables selection based on dynamic and lineage-dependent cellular behaviors, thereby addressing an important gap in current single-cell screening technologies.

   A key strength is the effective integration of mother machine microfluidics, custom optics, and deep-learning-based tracking into an automated and functional system. While the individual components are established, their combination into a phenotype-driven selection platform is innovative and expands the utility of live-cell microscopy from passive observation to active cell selection. The advance is therefore primarily methodological and technological, with potential to enable future conceptual discoveries in cellular heterogeneity and lineage dynamics.

   However, limitations remain regarding scalability, robustness, selection accuracy, and generalizability across biological systems. Additional benchmarking and validation would strengthen the work further.

   Overall, the study will be of interest to researchers in microfluidics, single-cell biology, microbial systems biology, bioengineering, quantitative imaging, and synthetic biology.

   My expertise is in microfluidics, cell sorting and disease mechanobiology.

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4. 

   Review Commons

   Jun 16, 2026

   Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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   Referee #1

   Evidence, reproducibility and clarity

   Summary:

   The authors present MiPS, a platform combining DMD-based patterned illumination, automated microscopy, retrained DeLTA segmentation, and mother-machine microfluidics to selectively inhibit or eliminate cells based on dynamic phenotypes. The system enables targeted UV or red-light illumination in real time using segmentation-informed projection masks, allowing selective enrichment directly within mother-machine devices. The manuscript demonstrates proof-of-concept enrichment of mCherry cells from mixed GFP/mCherry populations, characterizes off-target effects, and performs computational simulations of iterative enrichment …

   Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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   Referee #1

   Evidence, reproducibility and clarity

   Summary:

   The authors present MiPS, a platform combining DMD-based patterned illumination, automated microscopy, retrained DeLTA segmentation, and mother-machine microfluidics to selectively inhibit or eliminate cells based on dynamic phenotypes. The system enables targeted UV or red-light illumination in real time using segmentation-informed projection masks, allowing selective enrichment directly within mother-machine devices. The manuscript demonstrates proof-of-concept enrichment of mCherry cells from mixed GFP/mCherry populations, characterizes off-target effects, and performs computational simulations of iterative enrichment rounds. Overall, the engineering and systems integration are impressive, and the platform has strong potential for applications in directed evolution, biosensor optimization, and dynamic phenotype-based selection workflows.

   Overall, I believe the work is suitable for publication after minor revisions and clarification of several aspects of the manuscript. In particular, the paper would benefit from additional context in the Introduction and Methods sections, clearer positioning relative to existing platforms, improved figure readability/captions, and a more careful revision of the English throughout the manuscript.

   Major comments:

   1. The manuscript should better position MiPS relative to recent microscopy-based and DMD-enabled selection/control systems, particularly Lugagne et al., Nature Communications (2024), DOI: 10.1038/s41467-024-46361-1. That work also combines mother-machine microfluidics, DeLTA-based real-time image analysis, and DMD projection. The key distinction here appears to be physical selection/enrichment through targeted killing rather than optogenetic control, and this difference should be stated more explicitly.
   2. The manuscript currently compares MiPS mostly to FACS/MACS. However, the more relevant comparison may be recent image-based and microfluidic photoselection systems. A dedicated comparison table discussing throughput, temporal phenotyping, iterative selection, dynamic phenotype tracking, and enrichment capabilities would strengthen the paper.
   3. The enrichment experiment in Figure 4 represents a relatively simple classification problem (GFP vs mCherry). Since the proposed applications involve subtle continuous phenotypes, it would considerably strengthen the manuscript to include at least one experiment selecting for high vs. low expressors within a single fluorescent reporter population.
   4. The strongest enrichment result (~170-fold enrichment in Figure 5) is entirely simulation-based. Since the manuscript already states that ~45 min is sufficient between rounds for growth evaluation, a real 2-3-round enrichment experiment seems feasible and would substantially strengthen the platform's practical relevance. This experiment appears realistic within a relatively short time investment.
   5. The bimodal distributions in Figure 2 suggest that a fraction of cells may be stress-resistant rather than simply surviving randomly. It would be useful to discuss whether repeated rounds could progressively enrich UV-resistant subpopulations.
   6. The manuscript repeatedly uses the term "killed," although the data shown in Figures 2 and 4 mostly demonstrate strong growth arrest/inhibition. Please clarify how the cutoff of division rate <0.4 h⁻¹ was selected and whether an independent viability assay was performed.
   7. The off-target analysis in Figure 3 is one of the strongest parts of the paper and should probably be emphasized more. The conclusion that the dominant effects are global rather than local is interesting, but additional discussion about optical scattering, ROS diffusion, or device-wide coupling effects would strengthen the interpretation.
   8. UV exposure is inherently mutagenic in E. coli, and untargeted cells still receive a substantial fraction of the UV dose at high targeting fractions. Please discuss whether the MB/red-light modality may be preferable in applications where preserving genotype integrity is important.
   9. The manuscript discusses that methylene blue (MB) improves the on:off target ratio, but MB also appears to reduce baseline growth by ~40% even without red-light exposure. This is potentially important for iterative selection workflows. Please discuss whether this effect is reversible after washout and how rapidly cells recover.
   10. The manuscript states that the retrained DeLTA model used ~3,000 annotated fluorescence images, but no train/validation/test split or segmentation performance metrics are reported. Since segmentation directly impacts phenotype classification and projection targeting, these details are important for reproducibility.
   11. The manuscript would benefit from a stronger Methods description regarding DMD calibration, alignment procedures, projection accuracy validation, and computational timing requirements for the real-time analysis pipeline.

   Minor comments:

   1. In Figure 1, it would help to better distinguish the imaging optical path from the photoselection/UV projection path.
   2. The manuscript claims submicron projection precision (<0.5 µm), but it would help to relate this more directly to trench dimensions and actual biological targeting accuracy.
   3. In Figure 3, please include trench spacing and trench geometry information, since these parameters are important for interpreting local leakage and off-target illumination effects.
   4. The fitted off-target scaling factor (m = 0.26) becomes central to the simulation framework later in the paper, but no uncertainty or confidence interval is reported for this fit.
   5. In Figure 4, please clarify more explicitly how mixed or unidentified trenches were handled computationally before projection.
   6. The enrichment shift from 1:1 to 3.8:1 in Figure 4D is promising, but the number of biological replicates should be stated. If this were a single experiment, additional replicates with error bars would increase confidence in the enrichment result.
   7. Several figure captions would benefit from additional context and clearer definitions of technical terms and abbreviations. In multiple cases, interpreting the figure panels was difficult without returning to the main text.
   8. Please define all abbreviations directly in the figure captions, even if they are introduced earlier in the manuscript.
   9. In several figures, the color coding is not fully explained in the captions. Please make sure all colors, dashed lines, highlighted regions, and overlays are explicitly defined.
   10. The captions should more clearly describe what readers are expected to conclude from each figure, not only what is shown.
   11. Figure 2 caption issue: the manuscript references "Figure 2F," but Figure 2 only contains panels A-E.
   12. The manuscript does not currently clarify whether the software, DMD calibration routines, or retrained DeLTA weights will be publicly released. Clarifying code and software availability would improve reproducibility.
   13. There are several grammatical and readability issues throughout the manuscript. The technical ideas are strong, but some sentences are difficult to follow and would benefit from careful proofreading and language editing.

   Significance

   General assessment:

   This is a creative and technically impressive study that combines mother-machine microfluidics, automated microscopy, real-time image analysis, and DMD-based photoselection into a unified platform for dynamic, phenotype-based enrichment. The strongest aspects of the work are the systems integration, the quantitative characterization of off-target effects, and the conceptual demonstration that dynamic microscopy-derived phenotypes can be linked to physical enrichment workflows.

   The main limitations are that the biological validation remains largely proof-of-concept and the most compelling enrichment results are currently simulation-based rather than experimentally demonstrated across multiple rounds. In addition, the manuscript would benefit from stronger positioning relative to recent image-based and DMD-enabled microfluidic control systems.

   Advance:

   The study extends the field of single-cell microfluidics and image-based selection by introducing a platform that links longitudinal microscopy measurements directly to physical enrichment decisions within mother-machine devices. To my knowledge, the combination of iterative feedback-driven selection, DMD-based targeted elimination, and dynamic phenotype tracking in this context is novel.

   The closest related systems appear to be recent DMD-enabled mother-machine platforms for real-time optogenetic control, particularly those reported by Lugagne et al. (Nature Communications 2024, DOI: 10.1038/s41467-024-46361-1). However, MiPS introduces a distinct conceptual advance by using patterned illumination for selective enrichment/elimination rather than gene-expression modulation alone.

   The advance is primarily technical and conceptual, with potential downstream applications in directed evolution, synthetic biology, biosensor engineering, and dynamic phenotype screening workflows that are difficult or impossible to implement using FACS alone.

   Audience:

   The work will likely be of strongest interest to researchers working in synthetic biology, microfluidics, single-cell analysis, systems biology, bioengineering, and automated microscopy. It may also be of broader interest to communities developing dynamic phenotype screening technologies, closed-loop biological control systems, and next-generation directed evolution platforms.

   The audience is likely specialized but multidisciplinary, spanning both engineering-oriented and biology-oriented researchers. The methods and conceptual framework may also influence future development of automated selection systems beyond the specific mother-machine context.

   Expertise - My expertise includes:

   • Microfluidics
   • Synthetic biology
   • Single-cell systems
   • Automated microscopy
   • Real-time image analysis
   • Bioengineering platforms
   • Dynamic phenotype characterization

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5. 

   Version published to 10.64898/2025.12.03.692065 on bioRxiv

   Dec 5, 2025