High-content cellular imaging, transcriptomics, and proteomics data provide
rich and complementary views on the molecular layers of biology that influence
cellular states and function. However, the biological determinants through
which changes in multi-omics measurements influence cellular morphology have
not yet been systematically explored, and the degree to which cell imaging
could potentially enable the prediction of multi-omics directly from cell
imaging data is therefore currently unclear. Here, we address the question of
whether it is possible to predict bulk multi-omics measurements directly from
cell images using Image2Omics -- a deep learning approach that predicts
multi-omics in a cell population directly from high-content images stained with
multiplexed fluorescent dyes. We perform an experimental evaluation in
gene-edited macrophages derived from human induced pluripotent stem cell
(hiPSC) under multiple stimulation conditions and demonstrate that Image2Omics
achieves significantly better performance in predicting transcriptomics and
proteomics measurements directly from cell images than predictors based on the
mean observed training set abundance. We observed significant predictability of
abundances for 5903 (22.43%; 95% CI: 8.77%, 38.88%) and 5819 (22.11%; 95% CI:
10.40%, 38.08%) transcripts out of 26137 in M1 and M2-stimulated macrophages
respectively and for 1933 (38.77%; 95% CI: 36.94%, 39.85%) and 2055 (41.22%;
95% CI: 39.31%, 42.42%) proteins out of 4986 in M1 and M2-stimulated
macrophages respectively. Our results show that some transcript and protein
abundances are predictable from cell imaging and that cell imaging may
potentially, in some settings and depending on the mechanisms of interest and
desired performance threshold, even be a scalable and resource-efficient
substitute for multi-omics measurements.

使用 Image2Omics 深度学习方法，预测细胞群体内来自高含量显微图像的多重荧光染色数据；结果表明，细胞成像可能是多重组学测量的可替代方法。

多组学预测深度学习高内容细胞成像

Multi-omics Prediction from High-content Cellular Imaging with Deep  Learning

Metacells are disjoint and homogeneous groups of single-cell profiles,
representing discrete and highly granular cell states. Existing metacell
algorithms tend to use only one modality to infer metacells, even though
single-cell multi-omics datasets profile multiple molecular modalities within
the same cell. Here, we present \textbf{C}ross-M\textbf{O}dal
\textbf{E}mbedding for \textbf{M}etaCell Identification (COEM), which utilizes
an embedded space leveraging the information of both scATAC-seq and scRNA-seq
to perform aggregation, balancing the trade-off between fine resolution and
sufficient sequencing coverage. COEM outperforms the state-of-the-art method
SEACells by efficiently identifying accurate and well-separated metacells
across datasets with continuous and discrete cell types. Furthermore, COEM
significantly improves peak-to-gene association analyses, and facilitates
complex gene regulatory inference tasks.

该研究提出了一种名为 COEM 的算法，能够利用同一个细胞中多种分子测量值的信息来进行 metacell 的聚类，以实现更精确和分辨率更高的结果，并且有效地提高了基因调控的推断任务的性能。

COEM: 跨模态嵌入用于元细胞识别

COEM: Cross-Modal Embedding for MetaCell Identification

Combining different modalities of data from human tissues has been critical
in advancing biomedical research and personalised medical care. In this study,
we leverage a graph embedding model (i.e VGAE) to perform link prediction on
tissue-specific Gene-Gene Interaction (GGI) networks. Through ablation
experiments, we prove that the combination of multiple biological modalities
(i.e multi-omics) leads to powerful embeddings and better link prediction
performances. Our evaluation shows that the integration of gene methylation
profiles and RNA-sequencing data significantly improves the link prediction
performance. Overall, the combination of RNA-sequencing and gene methylation
data leads to a link prediction accuracy of 71% on GGI networks. By harnessing
graph representation learning on multi-omics data, our work brings novel
insights to the current literature on multi-omics integration in
bioinformatics.

本研究利用图嵌入模型（即 VGAE）对组织特异性基因基因相互作用网络进行链接预测。经过消融实验，我们证明了多个生物模态（即多组学）的组合会导致更强大的嵌入并提高链接预测性能。我们的结果表明，基因甲基化和 RNA 测序数据的整合显着提高了链接预测性能。总体而言，RNA 测序和基因甲基化数据的组合使得基因基因相互作用网络上的链接预测准确率达到了 71％。本研究通过对多组学数据的图表示学习，为当前有关生物信息学中多组学整合研究带来了新的见解。