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

Reviewer #1 (Public review):

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.

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.

Reviewer #2 (Public review):

Summary:

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).

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.

Reviewer #3 (Public review):

Summary:

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.

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 a 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.

Author response:

General Statements

We are grateful for Review Commons’ handling of our work. All three reviewers have delivered technical and informed critiques of the work, while also communicating their great interest, belief in the impact, and uniqueness of our work. Below we set out how we will address the reviewer’s comments – we believe we can satisfy all points, except for two of requests from one reviewer for further experimental applications of the platform (that go beyond what was claimed in our manuscript), which are discussed in Section 4 below.

Description of the planned revisions

Reviewer 1:

Major Comments

We will add further review of other DMD-based microscopy systems (point 1), and a table comparing to other sorting/selection approaches (point 2). We will further discuss (with added references to literature and past work) biochemical mechanisms of killing/mutagenesis from both Methylene Blue and UV (Points 5, 6, 8, 9) as well as their linking to device-wide phenomena via optical coupling (Point 7). We will add further explanation and code for the DeLTA image segmentation/tracking software and share on Github (Point 10), as well as methodological detail on setup/analysis pipeline (Point 11).

Minor Comments

We will update presentation aspects in Figures (Point 1,9) and captions (Point 7, 8,10,11), and methodological details relating to size of projection patterns and chip features (Point 2, 3). We will re-fit and provide error analysis for off-target effects (Point 4) as well as replicate details (Point 6). Similar to Major point 11, we will add details for analysis of empty/mixed trenches. As above we will publicly share the DMD/microscope software and DeLTA weights on a new Github project (Point 12). We will also edit throughout for grammar/readability.

Reviewer 2:

Major Comments

We will update statements to clarify that 300s is a maximum viable UV dosage based on time available at each imaging location while maintaining instrument throughput (Point 1), and highlight in the methods why this specific time (and those below it) was chosen (Point 2). We will expand the discussion relating to Methylene Blue’s biological effects as requested (Point 3) and also raised by Reviewer 1. We will add quantitative estimates of our technique’s viability for more unbalanced libraries, similar to what was done in the current final figure as requested (Point 4), and add a table to compare against other methods such as optical tweezers (as also suggested in Reviewer 1 Major pt 2).

Minor Comments

We will update Fig2F reference (Point 1), and typographic mistakes identified (Point 2).

Reviewer 3:

Major Comment

As also raised by Rev 1,2 we will add discussion of MB off-target toxicity, as well as experimental data relating to its impact on growth (eg as suggested through MIC concentration).

Minor Comments

We will add discussion of statistical tests used throughout where missing, here specifically Fig 3A (Point 1), and add error bars for FIg4D where missing (point 2). We will add further methdoological details (along with those requested by Rev 2) for the microfluidic device design.

Description of the revisions that have already been incorporated in the transferred manuscript

As described in other sections, I (Harrison Steel) will carry out all edits/additional analyses requested and welcome any further feedback from editors on these.

Description of analyses that authors prefer not to carry out

There are a couple of reviewer suggestions regarding further experimental development that, unfortunately, I cannot carry out. I would stress that these are points regarding possible extensions and applications beyond the claims we make in the manuscript - that is, they are not requests to repeat current experiments or perform additional controls etc to further verify any of claims in the submitted manuscript, but rather suggestions of how we could go beyond our current claims and scope of work. In particular, these are Reviewer 1’s major points 3 and 4 which suggest exploring other applications and longer multi-round experiments to strengthen the manuscript.

The reason we cannot carry out this significant set of experimental studies is that the three experimental authors of the paper (James, Sechkar, Towers, who were PhD students during the work) have now graduated and have gone on to jobs in other institutions/companies where they cannot work with the platform in my lab. Further, one of the postdoctoral co-authors (Kempf) has recently achieved independence as a new group leader and so is also not able to continue working in my laboratory, while another (Wang) is now employed in a different research group working in a different area. As such, my (Harrison Steel’s) team lacks the logistical ability (e.g. experimental researchers with expertise in this area) to do the extensions of the work proposed. We are currently recruiting a new PhD student to the team to further develop this platform - however the timeline for them to start, be trained, and meaningfully complete these experiments is >1.5 years, making it challenging for this review process.

I hope this challenging logistical consideration makes clear our circumstances and hence planned revisions above.