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The organization of cells within a tissue plays a critical role in tuning cellular function. Several methods have recently been developed to capture the transcriptome of cells while retaining spatial information. However, these genome-wide sequencing methods typically lack the spatial resolution of individual cells and are confined to quantifying positional information within predefined lattice locations, thereby failing to capture large sections of a tissue outside these regions. Further, these methods are generally limited to profiling fixed cells with reduced mRNA capture efficiency compared to standard scRNA-seq. In addition, existing methods lack modularity and cross-platform compatibility, thereby limiting most of these techniques from jointly profiling the epigenetic and transcriptomic state of individual cells. To overcome these limitations, we present scSTAMP-seq (single-cell Spatial Transcriptomic And Multiomic Profiling), an approach that employs cholesterol-tagged photolabile oligonucleotides that incorporate into cell membranes, enabling us to “stamp” the position of cells using spatially imposed light gradients prior to tissue dissociation and single-cell sequencing. Applied to live cells, scSTAMP-seq efficiently captures spatially resolved single-cell transcriptomes at high resolution for all cells within a field of view. Further, we demonstrate that light patterning enables dynamic spatial resolution, including the ability to map the position of individual cells. Finally, we show that scSTAMP-seq is modular and can be seamlessly integrated with various downstream single-cell sequencing technologies. We demonstrate this by performing scRNA-seq using plate- and droplet-based methods, and by performing joint epigenome and transcriptome sequencing from the same cell while preserving positional information. Collectively, these results demonstrate that scSTAMP-seq is a sensitive and high-throughput technology for mapping single-cell transcriptomes and epigenomes at the spatial resolution of individual cells.
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Single-cell RT-LAMP mRNA detection by integrated droplet sorting and merging
Recent advances in transcriptomic analysis at single-cell resolution reveal cell-to-cell heterogeneity in a biological sample with unprecedented resolution. Partitioning single cells in individual micro-droplets and harvesting each cell's mRNA molecules for next-generation sequencing has proven to be an effective method for profiling transcriptomes from a large number of cells at high throughput. However, the assays to recover the full transcriptomes are time-consuming in sample preparation and require expensive reagents and sequencing cost. Many biomedical applications, such as pathogen detection, prefer highly sensitive, reliable and low-cost detection of selected genes. Here, we present a droplet-based microfluidic platform that permits seamless on-chip droplet sorting and merging, which enables completing multi-step reaction assays within a short time. By sequentially adding lysis buffers and reactant mixtures to micro-droplet reactors, we developed a novel workflow of single-cell reverse transcription loop-mediated-isothermal amplification (scRT-LAMP) to quantify specific mRNA expression levels in different cell types within one hour. Including single cell encapsulation, sorting, lysing, reactant addition, and quantitative mRNA detection, the fully on-chip workflow provides a rapid, robust, and high-throughput experimental approach for a wide variety of biomedical studies.
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- Award ID(s):
- 1708706
- PAR ID:
- 10108056
- Date Published:
- Journal Name:
- Lab on a Chip
- Volume:
- 19
- Issue:
- 14
- ISSN:
- 1473-0197
- Page Range / eLocation ID:
- 2425 to 2434
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1039/C9LC00161A
