Our ability to connect genotypic variation to biologically important phenotypes has been seriously limited by the gap between live-cell microscopy and library-scale genomic engineering. Here, we show how in situ genotyping of a library of strains after time-lapse imaging in a microfluidic device overcomes this problem. We determine how 235 different CRISPR interference knockdowns impact the coordination of the replication and division cycles of Escherichia coli by monitoring the location of replication forks throughout on average >500 cell cycles per knockdown. Subsequent in situ genotyping allows us to map each phenotype distribution to a specific genetic perturbation to determine which genes are important for cell cycle control. The single-cell time-resolved assay allows us to determine the distribution of single-cell growth rates, cell division sizes and replication initiation volumes. The technology presented in this study enables genome-scale screens of most live-cell microscopy assays.
Your institute does not have access to this article
Open Access articles citing this article.
Nature Open Access 01 September 2021
Communications Biology Open Access 05 May 2021
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $9.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
The code used to analyze the DuMPLING microscopy images and generate figures associated with the DuMPLING experiments is provided as Supplementary Software.
Jinek, M. et al. A programmable dual-RNA–guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012).
Adli, M. The CRISPR tool kit for genome editing and beyond. Nat. Commun. 9, 1911 (2018).
Garst, A. D. et al. Genome-wide mapping of mutations at single-nucleotide resolution for protein, metabolic and genome engineering. Nat. Biotechnol. 35, 48–55 (2016).
Wang, H. H. et al. Programming cells by multiplex genome engineering and accelerated evolution. Nature 460, 894–898 (2009).
Rajagopal, N. et al. High-throughput mapping of regulatory DNA. Nat. Biotechnol. 34, 167–174 (2016).
Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e17 (2016).
Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882.e21 (2016).
Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896.e15 (2016).
Liu, Z., Lavis, L. D. & Betzig, E. Imaging live-cell dynamics and structure at the single-molecule level. Mol. Cell 58, 644–659 (2015).
Li, N. et al. Single-molecule imaging and tracking of molecular dynamics in living cells. Natl Sci. Rev. 4, 739–760 (2017).
Balzarotti, F. et al. Nanometer resolution imaging and tracking of fluorescent molecules with minimal photon fluxes. Science 355, 606–612 (2016).
Liu, T.-L. et al. Observing the cell in its native state: imaging subcellular dynamics in multicellular organisms. Science 360, eaaq1392 (2018).
Baltekin, Ö., Boucharin, A., Tano, E., Andersson, D. I. & Elf, J. Antibiotic susceptibility testing in less than 30 min using direct single-cell imaging. Proc. Natl Acad. Sci. USA 114, 9170–9175 (2017).
Hammar, P. et al. Direct measurement of transcription factor dissociation excludes a simple operator occupancy model for gene regulation. Nat. Genet. 46, 405–408 (2014).
Wang, P. et al. Robust growth of Escherichia coli. Curr. Biol. 20, 1099–1103 (2010).
Lawson, M. J. et al. In situ genotyping of a pooled strain library after characterizing complex phenotypes. Mol. Syst. Biol. 13, 947 (2017).
Emanuel, G., Moffitt, J. R. & Zhuang, X. High-throughput, image-based screening of pooled genetic-variant libraries. Nat. Methods 14, 1159–1162 (2017).
Wallden, M., Fange, D., Lundius, E. G., Baltekin, Ö. & Elf, J. The synchronization of replication and division cycles in individual E. coli cells. Cell 166, 729–739 (2016).
Si, F. et al. Mechanistic origin of cell-size control and homeostasis in bacteria. Curr. Biol. 29, 1760–1770 (2019).
Ghatak, S., King, Z. A., Sastry, A. & Palsson, B. O. The y-ome defines the 35% of Escherichia coli genes that lack experimental evidence of function. Nucleic Acids Res. 47, 2446–2454 (2019).
Schneiders, T. & Levy, S. B. MarA-mediated transcriptional repression of the rob promoter. J. Biol. Chem. 281, 10049–10055 (2006).
Ranefall, P., Sadanandan, S. K. & Wählby, C. Fast adaptive local thresholding based on ellipse fit. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) 205–208 (2016).
Magnusson, K. E. G., Jalden, J., Gilbert, P. M. & Blau, H. M. Global linking of cell tracks using the Viterbi algorithm. IEEE Trans. Med. Imaging 34, 911–929 (2015).
Olivo-Marin, J.-C. Extraction of spots in biological images using multiscale products. Pattern Recognit. 35, 1989–1996 (2002).
Jaqaman, K. et al. Robust single-particle tracking in live-cell time-lapse sequences. Nat. Methods 5, 695–702 (2008).
Keyamura, K. et al. The interaction of DiaA and DnaA regulates the replication cycle in E. coli by directly promoting ATP DnaA-specific initiation complexes. Genes Dev. 21, 2083–2099 (2007).
Katayama, T., Kasho, K. & Kawakami, H. The DnaA cycle in Escherichia coli: activation, function and inactivation of the initiator protein. Front. Microbiol. 8, 2496 (2017).
Saxena, R., Fingland, N., Patil, D., Sharma, A. K. & Crooke, E. Crosstalk between DnaA protein, the initiator of Escherichia coli chromosomal replication, and acidic phospholipids present in bacterial membranes. Int. J. Mol. Sci. 14, 8517–8537 (2013).
Camara, J. E. et al. Hda inactivation of DnaA is the predominant mechanism preventing hyperinitiation of Escherichia coli DNA replication. EMBO Rep. 6, 736–741 (2005).
Fujimitsu, K., Senriuchi, T. & Katayama, T. Specific genomic sequences of E. coli promote replicational initiation by directly reactivating ADP-DnaA. Genes Dev. 23, 1221–1233 (2009).
Kasho, K. & Katayama, T. DnaA binding locus datA promotes DnaA-ATP hydrolysis to enable cell cycle-coordinated replication initiation. Proc. Natl Acad. Sci. USA 110, 936–941 (2013).
Goldbeter, A. & Koshland, D. E. Jr. An amplified sensitivity arising from covalent modification in biological systems. Proc. Natl Acad. Sci. USA 78, 6840–6844 (1981).
Babic, A., Lindner, A. B., Vulic, M., Stewart, E. J. & Radman, M. Direct visualization of horizontal gene transfer. Science 319, 1533–1536 (2008).
Keseler, I. M. et al. The EcoCyc database: reflecting new knowledge about Escherichia coli K-12. Nucleic Acids Res. 45, D543–D550 (2017).
Goodall, E. C. A. et al. The essential genome of Escherichia coli K-12. mBio 9, e02096-17 (2018).
Jiang, H. & Wong, W. H. SeqMap: mapping massive amount of oligonucleotides to the genome. Bioinformatics 24, 2395–2396 (2008).
Markham, N. R. & Zuker, M. in Bioinformatics, Volume II: Structure, Function and Applications (ed. Keith, J. M.) Ch. 1, 3–31 (Humana Press, 2008).
Jones, D. L. et al. Kinetics of dCas9 target search in Escherichia coli. Science 357, 1420–1424 (2017).
Baba, T. et al. Construction of Escherichia coli K‐12 in‐frame, single‐gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 2006.0008 (2006).
Cherepanov, P. P. & Wackernagel, W. Gene disruption in Escherichia coli: TcR and KmR cassettes with the option of Flp-catalyzed excision of the antibiotic-resistance determinant. Gene 158, 9–14 (1995).
This work was supported by the Knut and Alice Wallenberg Foundation (2017.0291 and 2016.0077), the European Research Council (616047) and the Swedish Research Council (VR)(642-2013-7841 and 2016-06213). We are grateful to I. Barkefors for help with the manuscript and figures, to P. Karempudi for making microfluidic molds and to the Dan Andersson lab for kindly sharing E. coli strains.
The DuMPLING technology is patented with European patent no. EP3167061 (B1).
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
Image adapted from (Baltekin et al. 201713). The holes punched in the PDMS before bonding it to the PDMS are 2.0 (media in, front channel), 7.0 and 8.0 (waste port, front channel), 5.1 and 5.2 (waste port, back channel), 2.1 and 2.2 (cell loading or waste port, front channel).
Identical line style indicates three replicate cultures from three different colonies of the same plasmid construct in the test strain. No sgRNA: Negative sgRNA control plasmid pGuide-empty. lacY sgRNA: sgRNA against lacY from plasmid pGuide-P1-lacY. Glucose or lactose was used as supplemental carbon source and dCas9 expression/CRISPRi was induced with aTc (+ aTc). This assay was carried out once.
Supplementary Fig. 3 Comparison of relative abundances of genotypes in DuMPLING and NGS experiments.
Horizontal axis: Relative abundance of each strain as measured by next generation sequencing. (Raw sequence counts given in Supplementary Table 2) Vertical Axis: Relative abundance of each strain in the microfluidic chip. (The number of measurements for each genotype is equal to its number of traps as given in Supplementary Table 1.) Colors of dots indicate the growth rate of each genotype as estimated by NGS.
Histogram of average cell pixel intensity per trap after background reduction in logarithmic scale. The lower intensity fluorescence peaks are fitted to Gaussians (solid red line) and genotypes are determined when the probe fluorescence signal reaches above 7 times the standard deviations (dashed red lines) of the fitted Gaussians. See the Image analysis subsection on Genotyping in the Methods for details of how to determine genotypes.
A. (left) Fork distribution plot of ref strain (same data as Fig. 3e). Horizontal is SeqA-YFP cluster location along the long axis of the cell, vertical is cell size, color indicates the probability of finding a SeqA-YFP foci at a given position along the cell long axis for a given cell size. Initiation size (red dashed line) corresponds to the average of individually tracked replication forks. (right) Estimating initiation size (dashed red line) by fitting an error function (solid red line) to bulk data (blue squares) from the regions |x|>0.44 μm in the forkplot. B. (left) Same as Fig. 2c. (right) Estimating initiation size (dashed red line) by fitting a Gaussian (solid red line) to the relative histogram of single cell initiation sizes. C. Same as Fig. 3f in main text.
Correlation plots for normalized average growth rate (Pearson correlation = 0.86, n=215 different strains where growth rates were estimated in both run 1 and run 2), normalized average birth size (Pearson correlation = 0.78, n=215 different strains where birth sizes were estimated in both run 1 and run 2) and normalized average initiation size (Pearson correlation = 0.74, n=191 different strains where initiation sizes were estimated in both run 1 and run 2) between duplicate experiments. Deviations by more than 0.1 from the straight line where run 1 = run 2 are indicated by names. The number of data points used to estimate each average in the three different panels is given in Supplementary Table 1. Reference control strain (ref) is indicated with red dots.
Supplementary Figs. 1–12 and Notes 1–4.
Example of time-lapse fluorescence imaging; one position with segmented cell outlines (yellow) and detected SeqA-YFP clusters (red). The video is truncated in time compared with the complete experiment.
Collage of time-lapse phase contrast imaging; from 1/3 of the positions in one experiment. The video is truncated in time compared with the complete experiment.
Collage of time-lapse fluorescence imaging; from 1/3 of the positions in one experiment. The video is truncated in time compared with the complete experiment.
Fluorescence time-lapse imaging of three cell traps genotyped as ref traps; images are overlaid with segmented cell outlines (yellow) and detected SeqA-YFP clusters (red). There is 2 min between fluorescence images in the experiment. The video playback rate is 7 fps.
Fluorescence time-lapse imaging of three cell traps genotyped as dedD traps; images are overlaid with segmented cell outlines (yellow) and detected SeqA-YFP clusters (red). There is 2 min between fluorescence images in the experiment. The video playback rate is 7 fps.
Fluorescence time-lapse imaging of three cell traps genotyped as hda traps; images are overlaid with segmented cell outlines (yellow) and detected SeqA-YFP clusters (red). There is 2 min between fluorescence images in the experiment. The video playback rate is 7 fps.
Fluorescence time-lapse imaging of three cell traps genotyped as clpP traps; images are overlaid with segmented cell outlines (yellow) and detected SeqA-YFP clusters (red). There is 2 min between fluorescence images in the experiment. The video playback rate is 7 fps.
Time-lapse phase contrast imaging of three traps with single-gene clpP knockdown cells; there is 5 min between phase contrast images in the experiment. The video playback is at 7 fps.
Time-lapse phase contrast imaging of three traps with single-gene clpP knockout cells; there is 5 min between phase contrast images in the experiment. The video playback is at 7 fps.
DuMPLING data and statistics; estimates of mean and CV of growth rate, birth size and initiation size for each genotype in two replicates of the DuMPLING microscopy experiments (shown in two different tabs, ‘run 1’ and ‘run 2’). Each estimate is normalized to the ref strain in each experiment. The table also includes the number of data points used in each estimate (N cells) and the number of traps (N traps) that each detected genotype occupies. For initiation size bulk, the column ‘N cell detections’ implies the total number of detected SeqA-YFP foci used in fitting the initiation size.
NGS statistics; the number of occurrences of each strain before (t = 0) and after (t = 12 h) pooled competition is indicated, as well as the corresponding fractional abundances calculated from the numbers of occurrences. The resulting changes in relative abundance and relative fitness are also given, calculated as described in Supplementary Note 3.
Comparison of DuMPLING experiments with single-gene knockdown and knockouts; for nine selected genotypes. Table includes normalized means, normalized CV and the number of data points used to estimate these (N cells).
List of genes in each of the transcriptional units of the CRISPRi target genes in the DuMPLING library; the table also includes genes in each transcriptional unit that have been reported essential.
Sequences of CRISPRi spacers, barcodes and oligonucleotides; for each genotype in the DuMPLING library.
Strains used in this study; includes genotypes and description.
Compressed file with all software used to derive and analyze the DuMPLING data.
About this article
Cite this article
Camsund, D., Lawson, M.J., Larsson, J. et al. Time-resolved imaging-based CRISPRi screening. Nat Methods 17, 86–92 (2020). https://doi.org/10.1038/s41592-019-0629-y
Nature Protocols (2022)
Nature Protocols (2022)
Nature Reviews Methods Primers (2022)
Nature Methods (2021)
Nature Methods (2021)