Opto-magnetic capture of individual cells based on visual phenotypes

Video-based pooled screening yields improved far-red genetically encoded voltage indicators

Video-based screening of pooled libraries is a powerful approach for directed evolution of biosensors because it enables selection along multiple dimensions simultaneously from large libraries. Here we develop a screening platform, Photopick, which achieves precise phenotype-activated photoselection over a large field of view (2.3 × 2.3 mm, containing >103 cells, per shot). We used the Photopick platform to evolve archaerhodopsin-derived genetically encoded voltage indicators (GEVIs) with improved signal-to-noise ratio (QuasAr6a) and kinetics (QuasAr6b). These GEVIs gave improved signals in cultured neurons and in live mouse brains. By combining targeted in vivo optogenetic stimulation with high-precision voltage imaging, we characterized inhibitory synaptic coupling between individual cortical NDNF (neuron-derived neurotrophic factor) interneurons, and excitatory electrical synapses between individual hippocampal parvalbumin neurons. The QuasAr6 GEVIs are powerful tools for all-optical electrophysiology and the Photopick approach could be adapted to evolve a broad range of biosensors.

Migration speed of captured breast cancer subpopulations correlates with metastatic fitness

The genetic alterations contributing to migration proficiency, a phenotypic hallmark of metastatic cells required for colonizing distant organs, remain poorly defined. Here, we used single-cell magneto-optical capture (scMOCa) to isolate fast cells from heterogeneous human breast cancer cell populations, based on their migratory ability alone. We show that captured fast cell subpopulations retain higher migration speed and focal adhesion dynamics over many generations as a result of a motility-related transcriptomic profile. Upregulated genes in isolated fast cells encoded integrin subunits, proto-cadherins and numerous other genes associated with cell migration. Dysregulation of several of these genes correlates with poor survival outcomes in people with breast cancer, and primary tumors established from fast cells generated a higher number of circulating tumor cells and soft tissue metastases in pre-clinical mouse models. Subpopulations of cells selected for a highly migratory phenotype demonstrated an increased fitness for metastasis.

Laser-Assisted Single-Cell Labeling and Capture

Single-cell technologies have become critical tools to understand and characterize the complex dynamics that govern biological systems, from embryonic development to cancer heterogeneity. In this context, identification and capture of live individual cells in heterogenous ensembles typically rely on genetic manipulations that encode fluorescent probes. However, a precise understanding of how several molecular components interact to yield the phenotype of interest is a prerequisite to distinguishing and isolating such target cells based on fluorescence alone. Indeed, cellular phenotypes associated with migration, shape, location, or intracellular protein distribution play critical and well-understood roles in cancer biology, but the technologies to tag and isolate cells based on information obtained from imaging are not readily available.Cell labeling via photobleaching (CLaP) and single-cell magneto-optical capture (scMOCa) represent convenient and cost-effective systems for labeling, capturing, and expanding single cells from a heterogenous population, without altering cellular physiology and therefore enabling not only transcriptomic profiling but also biological characterization of target cells. Both techniques allow capturing cells after observation and permit researchers to choose target cells based on information obtained from images. The implementation of these technologies only needs the lasers of a confocal microscope and low-cost, commercially available chemical reagents. Here, we describe a detailed protocol to set up and perform CLaP and scMOCa and highlight critical points for optimal performance.

Advances in precise single-cell capture for analysis and biological applications

Cells are the basic structural and functional units of living organisms. However, conventional cell analysis only averages millions of cell populations, and some important information is lost. It is essential to quantitatively characterize the physiology and pathology of single-cell activities. Precise single-cell capture is an extremely challenging task during cell sample preparation. In this review, we summarize the category of technologies to capture single cells precisely with a focus on the latest development in the last five years. Each technology has its own set of benefits and specific challenges, which provide opportunities for researchers in different fields. Accordingly, we introduce the applications of captured single cells in cancer diagnosis, analysis of metabolism and secretion, and disease treatment. Finally, some perspectives are provided on the current development trends, future research directions, and challenges of single-cell capture.

Image-based pooled whole-genome CRISPRi screening for subcellular phenotypes

Genome-wide CRISPR screens have transformed our ability to systematically interrogate human gene function, but are currently limited to a subset of cellular phenotypes. We report a novel pooled screening approach for a wider range of cellular and subtle subcellular phenotypes. Machine learning and convolutional neural network models are trained on the subcellular phenotype to be queried. Genome-wide screening then utilizes cells stably expressing dCas9-KRAB (CRISPRi), photoactivatable fluorescent protein (PA-mCherry), and a lentiviral guide RNA (gRNA) pool. Cells are screened by using microscopy and classified by artificial intelligence (AI) algorithms, which precisely identify the genetically altered phenotype. Cells with the phenotype of interest are photoactivated and isolated via flow cytometry, and the gRNAs are identified by sequencing. A proof-of-concept screen accurately identified PINK1 as essential for Parkin recruitment to mitochondria. A genome-wide screen identified factors mediating TFEB relocation from the nucleus to the cytosol upon prolonged starvation. Twenty-one of the 64 hits called by the neural network model were independently validated, revealing new effectors of TFEB subcellular localization. This approach, AI-photoswitchable screening (AI-PS), offers a novel screening platform capable of classifying a broad range of mammalian subcellular morphologies, an approach largely unattainable with current methodologies at genome-wide scale.

Versatile phenotype-activated cell sorting

Unraveling the genetic and epigenetic determinants of phenotypes is critical for understanding and re-engineering biology and would benefit from improved methods to separate cells based on phenotypes. Here, we report SPOTlight, a versatile high-throughput technique to isolate individual yeast or human cells with unique spatiotemporal profiles from heterogeneous populations. SPOTlight relies on imaging visual phenotypes by microscopy, precise optical tagging of single target cells, and retrieval of tagged cells by fluorescence-activated cell sorting. To illustrate SPOTlight's ability to screen cells based on temporal properties, we chose to develop a photostable yellow fluorescent protein for extended imaging experiments. We screened 3 million cells expressing mutagenesis libraries and identified a bright new variant, mGold, that is the most photostable yellow fluorescent protein reported to date. We anticipate that the versatility of SPOTlight will facilitate its deployment to decipher the rules of life, understand diseases, and engineer new molecules and cells.

High-throughput, microscope-based sorting to dissect cellular heterogeneity

Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype. Automated imaging and phenotypic analysis directs selective illumination of Dendra2, a photoconvertible fluorescent protein expressed in live cells; these photoactivated cells are then isolated using fluorescence-activated cell sorting. First, we use Visual Cell Sorting to assess hundreds of nuclear localization sequence variants in a pooled format, identifying variants that improve nuclear localization and enabling annotation of nuclear localization sequences in thousands of human proteins. Second, we recover cells that retain normal nuclear morphologies after paclitaxel treatment, and then derive their single-cell transcriptomes to identify pathways associated with paclitaxel resistance in cancers. Unlike alternative methods, Visual Cell Sorting depends on inexpensive reagents and commercially available hardware. As such, it can be readily deployed to uncover the relationships between visual cellular phenotypes and internal states, including genotypes and gene expression programs.

Opto-magnetic Selection and Isolation of Single Cells

Capturing single cells from large heterogenous populations based solely on observable traits is necessary for many cell biology applications and remains a major technical challenge. The protocol we present allows the isolation of viable and metabolically active cells selected for their shape, migration speed, contact to other cells, or intracellular protein localization. We previously introduced a method termed Cell Labeling via Photobleaching (CLaP) for the efficient tagging of cells chosen for visual criteria. Here we describe a new protocol for capturing such cells using ferromagnetic beads termed single-cell magneto-optical capture (scMOCa). This technology is especially useful when the number of target cells represents an extremely low fraction of the total population (potentially one single cell), a situation in which conventional sorting techniques like fluorescent or magnetic activated cell sorting (F/MACS) cannot provide satisfactory results in terms of capture efficiency and specificity. scMOCa uses the lasers of a confocal microscope to photobleach and crosslink biotin-4-fluorecein molecules to cell membranes. Streptavidin coated magnetic beads then adhere to biotin moieties and a magnet allows the capture of illuminated cells. By precisely controlling liquid volumes and spacing between the different parts of a simple setup, high cell selectivity and capture efficacy can be achieved. scMOCA allows visual selection and isolation of any number of cells in a microscopy field and captured cells remain viable to generate new colonies of chosen phenotypes for downstream analyses.