SRT: Spatially Resolved Transcriptomics

Bulk cell

RNA-Seq

Single cell

RNA-Seq

Spatially Resolved

Transcriptomics

What and Why

https://twitter.com/BoXia7/status/1261464021322137600

Category

NGS-based

Imaging-based

• smFISH, seqFISH, MERFISH
• Super high spatial resolution.

• Geo-seq, Visium, DBiT-seq
• unbiased capture

Marx V. Method of the Year: spatially resolved transcriptomics[J]. Nature methods, 2021, 18(1): 9-14.

History and tendency

• Higher spatial resolution.
• Capture more genes.
• Larger field of view.

Cheng M, Jiang Y, Xu J, et al. Spatially resolved transcriptomics: a comprehensive review of their technological advances, applications, and challenges[J]. Journal of Genetics and Genomics, 2023.

Why imaging based?

Seeing is believing!

眼见为实！

smFISH

Farack L, Itzkovitz S. Protocol for single-molecule fluorescence in situ hybridization for intact pancreatic tissue[J]. STAR protocols, 2020, 1(1): 100007.

seqFISH and seqFISH+

https://spatial.caltech.edu/seqfish/

Eng C H L, Lawson M, Zhu Q, et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+[J]. Nature, 2019, 568(7751): 235-239.

seqFISH

seqFISH+

MERFISH

Chen K H, Boettiger A N, Moffitt J R, et al. Spatially resolved, highly multiplexed RNA profiling in single cells[J]. Science, 2015, 348(6233): aaa6090.

ExSeq

Alon S, Goodwin D R, Sinha A, et al. Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems[J]. Science, 2021, 371(6528): eaax2656.

MiP-Seq

Wu X, Xu W, Deng L, et al. Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics[J]. bioRxiv, 2023: 2023.01. 07.523058.

Commercial solutions

Upstream analysis

• Overview
• Bioimage processing
  • Image reconstruction
  • Image registration
  • Cell segmentation
• Tool and frameworks for upstream analysis
  • Starfish
  • PipeFISH

Overview

Preprocessing: Image reconstruction

Content-aware image restoration(CARE) based on deep learning extends the range of biological phenomena observable by microscopy.

Weigert M, Schmidt U, Boothe T, et al. Content-aware image restoration: pushing the limits of fluorescence microscopy[J]. Nature methods, 2018, 15(12): 1090-1097.

Use case: Thick tissue MERFISH

Fang R, Halpern A R, Rahman M M, et al. Three-dimensional single-cell transcriptome imaging of thick tissues[J]. bioRxiv, 2023: 2023.07. 21.550124.

Preprocessing: Image registration

Challenge:

• Large scale registration
• Nonlinear deformation between rounds.

Spots Calling

Challenge:

• Accuracy!
• Workload of parameter adjustment.
• Optical crowding in high density region.

Bahry E, Breimann L, Zouinkhi M, et al. RS-FISH: precise, interactive, fast, and scalable FISH spot detection[J]. Nature Methods, 2022, 19(12): 1563-1567.

Spots Calling: U-FISH

Un-published work

Cell segmentation

Challenges

• 3d segmentation
• DAPI staining does not represent cell boundaries.

cellpose

Stringer C, Wang T, Michaelos M, et al. Cellpose: a generalist algorithm for cellular segmentation[J]. Nature methods, 2021, 18(1): 100-106.

Decode

Challenges

• Crowded signal

• The balance issue between channels.

GraphISS

Partel G, Hilscher M M, Milli G, et al. Automated identification of the mouse brain’s spatial compartments from in situ sequencing data[J]. BMC biology, 2020, 18(1): 1-14.

gene-to-cell

Challenges

• DAPI staining does not represent cell boundaries.
• The ambiguous area between two cells.

SCS: cell segmentation for high-resolution spatial transcriptomics
 

Framework and tools for upstream analysis

• Starfish
• PIPEFISH
• ImageJ ecosystem
  • RS-FISH
  • ...
• Python ecosystem

Chanllenges

• Scalability
• Universality
• User-friendly

Starfish

https://github.com/spacetx/starfish

PIPEFISH

Cisar C, Keener N, Ruffalo M, et al. A unified pipeline for FISH spatial transcriptomics[J]. Cell Genomics, 2023, 3(9).

ImageJ ecosystem

• Spots calling: RS-FISH
• Image stiching: fiji/Stitching
• Visualization: Bigdata viewer
• Image registration: BigWrap
• ...

Python ecosystem

• Image processing: numpy, scipy, skimage
• Visualization: Napari viewer
• Image registration: itk-elastix
• Deep learning: Pytorch, Tensorflow
• ...

ImgFlow

• Node editor based image processing
• Based on Python ecosystem
• Composability

Un-published work

Downstream analysis

• Some common spatial analysis

• Integration with single-cell sequencing data

• Subcellular level analysis

• Alignment between different slides/experiments

• Multi-modality integration

Common spatial analysis

Add spatial information to the analysis of single-cell RNA-Seq

• Spatial Variable Genes

• Spatial domain

• Spatial trajectory

• Spatial cell-cell interaction

• ...

Hu J, Li X, Coleman K, et al. SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network[J]. Nature methods, 2021, 18(11): 1342-1351.

Integration with single-cell sequencing data

Overcoming limitations in gene throughput.

Biancalani T, Scalia G, Buffoni L, et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram[J]. Nature methods, 2021, 18(11): 1352-1362.

Li B, Zhang W, Guo C, et al. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution[J]. Nature methods, 2022, 19(6): 662-670.

Subcellular analysis

Find the pattern in subcellular level.

Xia C, Fan J, Emanuel G, et al. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression[J]. Proceedings of the National Academy of Sciences, 2019, 116(39): 19490-19499.

Mah C K, Ahmed N, Lopez N, et al. Bento: A toolkit for subcellular analysis of spatial transcriptomics data[J]. BioRxiv, 2022: 2022.06. 10.495510.

Alignment between different slides/experiments

Xia C R, Cao Z J, Tu X M, et al. Spatial-linked alignment tool (SLAT) for aligning heterogenous slices[J]. Nature Communications, 2023, 14(1): 7236.

Clifton K, Anant M, Aihara G, et al. Alignment of spatial transcriptomics data using diffeomorphic metric mapping[J]. bioRxiv, 2023.

Multi-modality integration

SRT + ?

Wu X, Xu W, Deng L, et al. Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics[J]. bioRxiv, 2023: 2023.01. 07.523058.