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Одноклеточное секвенирование�(single cell RNA-seq, scRNA-seq)

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Благодарности за слайды:

Антонов Иван Валентинович

Зубрицкий Анатолий

Simon Andrews (simon.andrews@babraham.ac.uk)

Åsa Björklund (asa.bjorklund@scilifelab.se)

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Bulk vs. Single Cell RNA-Seq

Ткани - гетерогенны!

Несмотря на то, что клетки морфологически идентичны, они могут иметь отличающиеся уровни экспрессии некоторых генов.

Это делает ткани и популяции клеток гетерогенными на уровне транскриптома и, иногда, генома (иммунные).

Экспрессия РНК в ткани = “средняя температура по больнице”

Уровень РНК из фрагмента кишечника - это уровень РНК из какого источника?

• Стволовые клетки.
• Эпителий
• Бокаловидные клетки
• Кровеносные сосуды
• Лимфатические
• Соединительная ткань
• Мускулатура
• Нейроны
• Симбиотические бактерии

https://medicine.nus.edu.sg/pathweb/normal-histology/colon/

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Почему single cell?

Jovic D. et al. Single‐cell RNA sequencing technologies and applications: A brief overview //Clinical and translational medicine. – 2022.

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Сравнение методов

10x Genomics

Гелевые шарики в эмульсии�(Gel Bead-in-Emulsion, GEMs)

https://theseuslab.by/p100390910-stantsiya-dlya-raboty.html

How 10X RNA-Seq Works

Oligo dT

Cell barcode (same within GEM)

UMI (all different)

Priming site

How 10X RNA-Seq Works

Read 1

Read 2

Read 3

How 10X RNA-Seq Works

Oligo dT

Cell barcode (same within GEM)

UMI (all different)

Priming site

AAAAAGATTCGTAGTGCTGATGCT...

Reverse Transcription

Mix RNAs

and Cells

Illumina Library Prep

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Одноклеточное секвенирование – эпигенетика

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Баркод молекулы (unique molecular indexes, UMI)

https://www.pnas.org/doi/full/10.1073/pnas.1118018109

Bulk vs. Single Cell RNA-Seq

Визуализация scRNA-seq -- PCA, t-SNE …

Визуализация scRNA-seq -- PCA, t-SNE …

Проекты по секвенированию всех типов клеток scRNA-seq

GTex

Human Cell Atlas

Перепрограммирование клеток – нам нужны модели

Для чего нужно одноклеточное секвенирование в RNA-seq

Для чего нужно одноклеточное секвенирование в эпигенетике

Одноклеточные методы секвенирования по эпигенетике

Одноклеточные методы секвенирования по эпигенетике

Одноклеточные методы секвенирования по эпигенетике

Одноклеточные методы секвенирования по эпигенетике

Одноклеточные методы секвенирования по эпигенетике

https://en.wikipedia.org/wiki/Single_cell_epigenomics

(1) fixed and permeabilized cells or nuclei are transposed by Tn5 to mark regions of open chromatin;

(2) mRNA is reverse transcribed using a poly(T) primer containing a UMI and a biotin tag;

(3) cells are distributed in a 96-well plate to hybridize well-specific barcoded oligonucleotides to transposed chromatin fragments and poly(T) cDNA;

(4) hybridization is repeated three times, expanding the barcoding space to approximately 10⁶ (96³) barcode combinations , and, following hybridization, cell barcodes are ligated simultaneously to cDNA and chromatin fragments;

(5) reverse crosslinking is performed to release barcoded molecules;

(6) cDNA is specifically separated from chromatin using streptavidin beads, and each library is prepared for sequencing;

(7) paired profiles are identified using the common combination of well-specific barcodes

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SHARE-seq

10x genomics

Chromium Single Cell Multiome ATAC + Gene Expression

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Cellranger ARC

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https://www.10xgenomics.com/support/software/cell-ranger-arc/latest

https://www.10xgenomics.com/support/software/cell-ranger-arc/2.0/analysis/multiome-atac-gene-expression-analysis

Chromatin accessibility and gene expression

within the same cell (CHARM)

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Extra

single-cell multi-omic data integration

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adapted from

Systems Biology Course (EMBL-EBI) - 25th October 2023 by Valentina Lorenzi

why do we want single-cell and multi-omic measurements?

cell intrinsic

cell extrinsic

The Human Cell Atlas White Paper

goals of multi-omic data analysis

embedding in a meaningful latent space

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identify statistical relationships between features

defining the integration axis

Arguelaguet, Cuomo, Stegle and Marioni (2021) Computational principles and challenges in single-cell data integration, Nat Biotechnology

defining the integration axis

Batch effect correction, mapping to reference atlas

Multi-omics analysis

Vertical integration of matched multi-omics data

1. Construct kNN graph on each modality’s low dimensional embedding

a. scRNA-seq --> PCA

b. scATAC-seq --> LSI

2. For each cell i, identify its k nearest neighbours in each modality (RNA neighbours and ATAC neighbours) and average the low-dimensional profile of each neighbour set, which represents a prediction for the molecular contents for cell i based on local neighbourhood

a. within-modality prediction

b. cross-modality prediction

3. Compute similarity between predicted values (within and cross-modality) and the actual low- dimensional profile of cell i
4. Calculate ratio between the two similarities (affinities) to obtain cell-specific modality weights
5. Compute new similarity metric between any two cells which reflects a weighted combination of RNA and ATAC affinities
6. Construct kNN graph using this weighted similarity metric (WNN)
7. Downstream analysis (i.e. visualization, clustering, etc.) of the WNN graph

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Is the average really the best view? Jointly analyzing datasets is supposed to increase the resolution, but we might just be smoothing out true differences between modalities

Hao, Hao et al. Cell 2021

weighted nearest neighbours

Vertical integration of matched multi-omics data

Multi-Omics Factor Analysis v2 (MOFA+)

Scalability limits

Affected by imbalances in number of features in each modality

Z = contains low dimensional representation of the cells

W = contains an association score for each feature with each latent factor

Structure of the data is specified in the prior distributions of the Bayesian

model

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Uses sparsity priors, which enable

automatic relevance determination of the factors

encourages solutions where factors are associated with a small number of features / active in few groups of

cells

Argelaguet, Velten et al. Mol Sys Biol 2018

Argelaguet, Arnol, Bredikhin et al. Genome Biology 2020

Diagonal integration of unmatched multi-omics data

adapted methods from horizontal integration

1. Transform data to gene-level features (e.g. count ATAC fragments over gene bodies)
2. Apply horizontal integration methods used for batch correction (e.g. Seurat’s CCA)

gene activity scores

Horizontal integration!

Assumption that gene accessibility is linearly correlated with gene expression

Stuart, Butler et al., Cell 2019

Diagonal integration of unmatched multi-omics data

autoencoder neural network architecture

matching graph topology

methods working on unpaired features

The embedding of each dataset is performed using an autoencoder, whose architectures can be customized to the specific data modality

Combining the encoder and decoder modules of different autoencoders enables translation between different data modalities at the single-cell level

1. recovers geodesic distances on a single latent manifold on which all data lie
2. constructs a neighborhood graph (MultiGraph) on the manifold
3. projects the data into a single low-dimensional embedding

Assumption that cells lie on the same latent manifold

Yang, Beyalaeva et al., Nature Communications 2021 Jain, Polanski et al., Genome Biology 2021

goals of multi-omic data analysis

embedding in a meaningful latent space

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identify statistical relationships between features

identifying statistical relationships between features

Network representations of molecular interactions between transcriptional regulators and target genes

With single-cell multi-omics we measure different molecular features / layers of gene regulation (either from the same cells or from different cells that can be computationally matched)

How do we identify statistical relationships between the different molecular features?

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In the case of scRNA/ATAC-seq multi-omic analysis this analysis is often referred to as

Gene Regulatory Network inference

Литература

Mani, S., Lalani, S.R. & Pammi, M. Genomics and multiomics in the age of precision medicine. Pediatr Res 97, 1399–1410 (2025). https://doi.org/10.1038/s41390-025-04021-0

Baysoy, A., Bai, Z., Satija, R. et al. The technological landscape and applications of single-cell multi-omics. Nat Rev Mol Cell Biol 24, 695–713 (2023). https://doi.org/10.1038/s41580-023-00615-w

Baião, Cai,Poulos, Robinson, Reddel, Zhong, Vinga, Gonçalves, A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches, Briefings in Bioinformatics, Volume 26, Issue 4, July 2025, bbaf355, https://doi.org/10.1093/bib/bbaf355

Lee, J., Hyeon, D.Y. & Hwang, D. Single-cell multiomics: technologies and data analysis methods. Exp Mol Med 52, 1428–1442 (2020). https://doi.org/10.1038/s12276-020-0420-2

Sai Ma et al Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin

https://doi.org/10.1016/j.cell.2020.09.056

Chen, Y., Liu, Z., Xu, H. et al. Gene regulatory landscape dissected by single-cell four-omics sequencing. Nature (2026). https://doi.org/10.1038/s41586-026-10322-z

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