Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays

Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data. 

/pdf/squidpy-a-scalable-framework-for-spatial-omics-analysis-1zth2qvj.pdf

Squidpy: a scalable framework for spatial omics analysis

The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future. 

/pdf/methods-and-applications-for-single-cell-and-spatial-multi-2e1h6coc.pdf

Methods and applications for single-cell and spatial multi-omics

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices. 

https://www.nature.com/articles/s41576-023-00586-w.pdf

Best practices for single-cell analysis across modalities

Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution

Charting an organs' biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. Tangram can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.

/pdf/deep-learning-and-alignment-of-spatially-resolved-single-34thuc012o.pdf

Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram.

Abstract Digitalisation of metallurgical manufacturing, especially technological continuous casting using numerical models of heat and mass transfer and subsequent solidification has been developed to achieve high manufacturing efficiency with minimum defects and hence low scrappage. It is still challenging to perform adaptive closed-loop process adjustment using high-fidelity computation in real-time. To address this challenge, surrogate models are a good option to replace the high-fidelity model, with acceptable accuracy and less computational time and cost. Based on deep learning technology, here we developed a real-time prediction (ReP) model to predict the three-dimensional (3D) temperature field distribution in continuous casting on millisecond timescale, with mean absolute error (MAE) of 4.19 K and mean absolute percent error (MAPE) of 0.49% on test data. Moreover, by combining the ReP model with machine learning technology—Bayesian optimisation, we realised the rapid decision-making intelligent adaptation of the operating parameters for continuous casting with high predictive capability. This innovative and reliable method has a great potential in the intelligent control of the metallurgical manufacturing process. 

/pdf/real-time-prediction-and-adaptive-adjustment-of-continuous-1cv5fnl9.pdf

Real-time prediction and adaptive adjustment of continuous casting based on deep learning

Molecular shape and geometry dictate key biophysical recognition processes, yet many graph neural networks disregard 3D information for molecular property prediction. Here, we propose a new contrastive-learning procedure for graph neural networks, Mol ecular C ontrastive L e a rning from S hape S imilarity ( MolCLaSS ), that implicitly learns a three-dimensional representation. Rather than directly encoding or targeting three-dimensional poses, MolCLaSS matches a similarity objective based on Gaussian overlays to learn a meaningful representation of molecular shape. We demonstrate how this framework naturally captures key aspects of three-dimensionality that two-dimensional representations cannot and provides an inductive framework for scaffold hopping.

A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning

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Papers

Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram.

Real-time prediction and adaptive adjustment of continuous casting based on deep learning

A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning