Single-cell in situ RNA profiling by sequential hybridization

440

483

Image-based approaches to single-cell transcriptomics, in which RNA species are identified and counted in situ via imaging, have emerged as a powerful complement to single-cell methods based on RNA sequencing of dissociated cells. These image-based approaches naturally preserve the native spatial context of RNAs within a cell and the organization of cells within tissue, which are important for addressing many biological questions. However, the throughput of these imagebased approaches is relatively low. Here we report advances that lead to a drastic increase in the measurement throughput of multiplexed error-robust fluorescence in situ hybridization (MERFISH), an imagebased approach to single-cell transcriptomics. In MERFISH, RNAs are identified via a combinatorial labeling approach that encodes RNA species with error-robust barcodes followed by sequential rounds of single-molecule fluorescence in situ hybridization (smFISH) to read out these barcodes. Here we increase the throughput of MERFISH by two orders of magnitude through a combination of improvements, including using chemical cleavage instead of photobleaching to remove fluorescent signals between consecutive rounds of smFISH imaging, increasing the imaging field of view, and using multicolor imaging. With these improvements, we performed RNA profiling in more than 100,000 human cells, with as many as 40,000 cells measured in a single 18-h measurement. This throughput should substantially extend the range of biological questions that can be addressed by MERFISH.single-cell analysis | fluorescence | in situ hybridization | transcriptomics | multiplexed imaging

Section: Discussionmentioning

confidence: 99%

High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization

Moffitt

Hao

Wang

et al. 2016

440

483

“…To extend the benefits of this technique to systems-level questions and high-throughput gene-expression profiling, approaches to increase the multiplexing of smFISH (i.e., the number of different RNA species that can be simultaneously quantified within the same cell) have been developed (8)(9)(10)(11)(12)(13). Most of these approaches take advantage of color multiplexing, which has allowed a few tens of RNA species to be imaged simultaneously.…”

mentioning

confidence: 99%

“…Recently, we have increased the measurement throughput of MERFISH and demonstrated the ability to profile gene expression in tens of thousands of cells in a single day-long measurement (14). A different multiplexed smFISH method using color-based barcodes and sequential imaging (seqFISH) has been independently proposed and initially demonstrated with measurements of 12 RNA species in individual cells (12). While this current paper was in review, a paper reporting an extension of seqFISH that adds the error-correction capability and demonstrates the capability of imaging 125 or 250 RNA species was published (15).…”

mentioning

confidence: 99%

High-performance multiplexed fluorescence in situ hybridization in culture and tissue with matrix imprinting and clearing

Moffitt

Hao

Bambah-Mukku

et al. 2016

241

249

Highly multiplexed single-molecule FISH has emerged as a promising approach to spatially resolved single-cell transcriptomics because of its ability to directly image and profile numerous RNA species in their native cellular context. However, backgroundfrom off-target binding of FISH probes and cellular autofluorescence-can become limiting in a number of important applications, such as increasing the degree of multiplexing, imaging shorter RNAs, and imaging tissue samples. Here, we developed a sample clearing approach for FISH measurements. We identified off-target binding of FISH probes to cellular components other than RNA, such as proteins, as a major source of background. To remove this source of background, we embedded samples in polyacrylamide, anchored RNAs to this polyacrylamide matrix, and cleared cellular proteins and lipids, which are also sources of autofluorescence. To demonstrate the efficacy of this approach, we measured the copy number of 130 RNA species in cleared samples using multiplexed error-robust FISH (MERFISH). We observed a reduction both in the background because of off-target probe binding and in the cellular autofluorescence without detectable loss in RNA. This process led to an improved detection efficiency and detection limit of MERFISH, and an increased measurement throughput via extension of MERFISH into four color channels. We further demonstrated MERFISH measurements of complex tissue samples from the mouse brain using this matrix-imprinting and -clearing approach. We envision that this method will improve the performance of a wide range of in situ hybridization-based techniques in both cell culture and tissues.tissue clearing | fluorescence in situ hybridization | multiplexed imaging | single-cell transcriptomics | brain S ingle-molecule FISH (smFISH) is a powerful technique that allows the direct imaging of individual RNA molecules within single cells (1, 2). In this approach, RNAs are labeled via the hybridization of fluorescently labeled oligonucleotide probes, producing bright fluorescent spots for single RNA molecules, which reveal both the abundance and the spatial distribution of these RNAs inside cells (1, 2). The ability of smFISH to image gene expression at the single-cell level in both cell culture and tissue has led to exciting advances in our understanding of the natural noise in gene expression and its role in cellular response (3, 4), the intracellular spatial organization of RNAs and its role in posttranscriptional regulation (5, 6), and the spatial variation in gene expression within complex tissues and its role in the molecular definition of cell types and tissue functions (6, 7).To extend the benefits of this technique to systems-level questions and high-throughput gene-expression profiling, approaches to increase the multiplexing of smFISH (i.e., the number of different RNA species that can be simultaneously quantified within the same cell) have been developed (8-13). Most of these approaches take advantage of color multiplexing, which has allowed a few tens...

“…Even more difficult is the task of capturing the precise time when a cell undergoes a cell-fate transition. Recently, new technologies are being rapidly developed to quantify gene expression at the single-cell resolution (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23), providing an unprecedented opportunity for the detection of such rare events. Nonetheless, interpretation of these novel kinds of data remains a difficult task owing to the lack of suitable computational methods.…”

mentioning

confidence: 99%

Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape

Marco

Karp

Guo

et al. 2014

259

241

We present single-cell clustering using bifurcation analysis (SCUBA), a novel computational method for extracting lineage relationships from single-cell gene expression data and modeling the dynamic changes associated with cell differentiation. SCUBA draws techniques from nonlinear dynamics and stochastic differential equation theories, providing a systematic framework for modeling complex processes involving multilineage specifications. By applying SCUBA to analyze two complementary, publicly available datasets we successfully reconstructed the cellular hierarchy during early development of mouse embryos, modeled the dynamic changes in gene expression patterns, and predicted the effects of perturbing key transcriptional regulators on inducing lineage biases. The results were robust with respect to experimental platform differences between RT-PCR and RNA sequencing. We selectively tested our predictions in Nanog mutants and found good agreement between SCUBA predictions and the experimental data. We further extended the utility of SCUBA by developing a method to reconstruct missing temporal-order information from a typical single-cell dataset. Analysis of a hematopoietic dataset suggests that our method is effective for reconstructing gene expression dynamics during human B-cell development. In summary, SCUBA provides a useful single-cell data analysis tool that is well-suited for the investigation of developmental processes.single cell | gene expression | bifurcation | differentiation