Single-cell genetic screens can be incredibly powerful, but current high-throughput platforms do not track dynamic processes, and even for non-dynamic properties they struggle to separate mutants of interest from phenotypic outliers of the wild-type population. Here we introduce SIFT, single-cell isolation following time-lapse imaging, to address these limitations. After imaging and tracking individual bacteria for tens of consecutive generations under tightly controlled growth conditions, cells of interest are isolated and propagated for downstream analysis, free of contamination and without genetic or physiological perturbations. This platform can characterize tens of thousands of cell lineages per day, making it possible to accurately screen complex phenotypes without the need for barcoding or genetic modifications. We applied SIFT to identify a set of ultraprecise synthetic gene oscillators, with circuit variants spanning a 30-fold range of average periods. This revealed novel design principles in synthetic biology and demonstrated the power of SIFT to reliably screen diverse dynamic phenotypes.
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The data that support the findings of this study are available from the corresponding authors upon request.
ImageJ macros for image segmentation and MATAB scripts for cell tracking and signal processing are available from the corresponding authors upon request.
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We thank R. Yuan for substantial help designing figure illustrations and S. Bakshi for sharing core image-analysis software and providing technical microscopy guidance. All photolithography was performed at the Center for Nanoscale Systems at Harvard University, a member of the National Nanotechnology Coordinated Infrastructure Network, which is supported by the National Science Foundation under award 1541959. All soft lithography was performed at the Microfluidics/Microfabrication Core Facility at Harvard Medical School. This work was supported by the Defense Advanced Research Projects Agency (HR0011-16-2-0049), National Science Foundation (1615487), and National Institutes of Health (R01GM081563).
S.L. and J.P. are listed as inventors on a provisional patent application covering the SIFT technology.
Peer review information Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
The trapped cell cohort was transported by optical trap for a range of times (Fig. 3d) and re-seeded as new mother cells in empty growth trenches. Trapped ancestors were mother cells within the same growth trench as transported cells were taken from. Cells that were not trapped included all other mother cells that were imaged during the recovery but were neither themselves nor their progeny exposed to the optical trap. General phenotypic classifications include mother cell death (no growth observed over a 12-h recovery and monitoring period), filamentation, undocumented loss (mother cell absence without observing an abnormal increase of cell length in the prior frame) and continual growth. Similar results were observed across three independent screening runs.
Statistical estimates of period CV and amplitude CV of all library variants with oscillatory-like signals over a 24-h period using SIFT (Nosc = 7,803). Variants selected to highlight phenotypic diversity (grey dots with roman numerals) have time-traces shown in Fig. 4c. Red circles represent variants that were isolated by SIFT, some of which were individually characterized in a follow-up mother machine run. Mean generation time was 25 min. Data were gathered from a single screening run.
Supplementary Figure 3 Sequences of mutated regions for all isolated cells from the dual-feedback library.
Numbering with ‘SL’ prefixes mark cells collected from the same lineage (that is, isolated from the same growth trench). Letterings A–C designate individual cells taken from the same clonal lineages. Three individual cells were isolated from each lineage, unless fewer than three cells were present at the time of isolation (for example, cells lost due to filamentation prior to collection). Mutated regulatory regions included araI1, the I1 binding site of AraC; lacO1, the binding site of LacI; and the ribosomal-binding site (RBS). The consensus sequence is shown at the top of each table. The top panel shows promoter variants controlling araC, while the bottom panel corresponds to lacI regulation. Asterisks denote agreement with non-degenerate sequences and dashes signify deletions.
Top panel shows period histograms of the original (SL126) and top-performing screened (SL278) dual-feedback circuits without chemical inducers. 6,170 periods are displayed from the original circuit and 3,054 periods from the isolate. Lower panel displays the autocorrelation functions (ACF) of the circuits from the top panel. Given the low signal-to-noise and limited fluctuation amplitudes of the original dual-feedback oscillator (see Fig. 4d), the autocovariance function (ACVF; not normalized by variance) is also shown for each of the two circuits in the inset.
Top panel shows period histograms of the original dual-feedback oscillator (SL126) with the optimized drug combination (0.7% arabinose, 2 mM IPTG); the circuit with constitutive araC mutation (Y13H; SL304) without drugs; and the top-performing screened (isolate; SL278) circuit without drugs. Histograms include 2,830 periods from the original circuit, 7,149 periods from the Y13H circuit, and 3,054 periods from the isolated circuit. Bottom panel displays the autocorrelation functions (ACF) of the circuits from the top panel.
All three libraries with period CV and mean period plotted on the same axes. The dominant-negative repressilator library with protein degradation (that is, with ClpXP present) was only characterized and was not screened. Variants with period CVs (CVT) below 20% are qualitatively deemed regular oscillators. Scatter plot displays 7,803 dual-feedback variants with degradation, 3,437 dominant-negative repressilator variants with degradation and 1,277 dominant-negative repressilator variants without degradation. Data for each library were gathered from a single screening run.
Supplementary Figure 7 Dominant-negative repressilator library amplitude to period noise relationship.
Statistical estimates of period CV and amplitude CV of all library variants with oscillatory-like signals over a 24-h period using SIFT (Nosc = 1,277). Variants selected to highlight phenotypic diversity (grey dots with roman numerals) have time-traces shown in Fig. 5d. Red circles represent variants that were isolated, some of which were individually characterized in a follow-up mother machine run. Mean generation time was 24 min. Data were gathered from a single screening run.
Supplementary Figure 8 Sequences of mutated regions for all isolated cells from the dominant-negative repressilator library.
Numbering with ‘SL’ prefixes mark cells collected from the same lineage (that is, isolated from the same growth trench). Letterings A–C designate individual cells taken from the same clonal lineages. Three individual cells were isolated from each lineage, unless fewer than three cells were present at the time of isolation (for example, cells lost due to filamentation prior to collection). Mutated regulatory regions included the –35 and –10 hexamers of the core promoter elements and the ribosomal-binding site (RBS) controlling expression of dominant-negative TetR. The consensus sequence is shown at the top of the table. Asterisks denote agreement with non-degenerate sequences and dashes signify deletions.
The mother machine data for characterizing a top-performing dominant-negative isolate (SL229 with a true period CV of 0.099) were parsed by individual time-traces and not pooled. Each trench-wise CV calculation was then sampled for 2 to 10 consecutive periods (only considering traces with 10 or more periods present; 141 traces). The trend in the CV variance as a function of the number of sampled periods highlights the chance of getting ‘false’ outlier CVs when just one lineage is monitored for oscillators with extremely long periods, as was the case for the dominant-negative repressilator screen, stressing the need for isolation followed by thorough characterization of these variants.
Supplementary Figure 10 Timing performance of an isolated dominant-negative repressilator with long-term oscillations.
Top panel shows period histograms of the original integrated repressilator (SL305) and a dominant-negative repressilator library isolate with lengthy coherent oscillations (SL224). Histograms include 8,161 periods from the original circuit and 886 periods from the isolated circuit. Bottom panel displays the autocorrelation functions (ACF) of the circuits from the top panel.
Supplementary Figures 1–10
Cell transport for screening. Three cells (third transit contains two cells stuck together) were individually transported via optical trapping from a single culture trench in the growth lane, moved across the perforated median (collection valves in open state) and dropped off in the collection lane. The movie was captured at 100× optical magnification and compiled at 15 frames per second. Cell transfers were performed more than 200 times across 5 independent screening runs with similar results.
Cell transport for re-seeding. Three cells were individually transported via optical trapping from a single culture trench in the growth lane, across the perforated median (collection valves in open state), and seeded within three new culture trenches in the collection lane. The movie was captured at 100× optical magnification and compiled at 15 frames per second. Cell re-seedings within collection lanes were performed more than 10 times across three independent screening runs with similar results.
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Luro, S., Potvin-Trottier, L., Okumus, B. et al. Isolating live cells after high-throughput, long-term, time-lapse microscopy. Nat Methods 17, 93–100 (2020). https://doi.org/10.1038/s41592-019-0620-7
Nature Methods (2021)
Nature Reviews Microbiology (2020)