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.

Single-cell genomics technologies enable multimodal profiling of millions of cells across temporal and spatial dimensions. Experimental limitations prevent the measurement of all-encompassing cellular states in their native temporal dynamics or spatial tissue niche. Optimal transport theory has emerged as a powerful tool to overcome such constraints, enabling the recovery of the original cellular context. However, most algorithmic implementations currently available have not kept up the pace with increasing dataset complexity, so that current methods are unable to incorporate multimodal information or scale to single-cell atlases. Here, we introduce multi-omics single-cell optimal transport (moscot), a general and scalable framework for optimal transport applications in single-cell genomics, supporting multimodality across all applications. We demonstrate moscot's ability to efficiently reconstruct developmental trajectories of 1.7 million cells of mouse embryos across 20 time points and identify driver genes for first heart field formation. The moscot formulation can be used to transport cells across spatial dimensions as well: To demonstrate this, we enrich spatial transcriptomics datasets by mapping multimodal information from single-cell profiles in a mouse liver sample, and align multiple coronal sections of the mouse brain. We then present moscot.spatiotemporal, a new approach that leverages gene expression across spatial and temporal dimensions to uncover the spatiotemporal dynamics of mouse embryogenesis. Finally, we disentangle lineage relationships in a novel murine, time-resolved pancreas development dataset using paired measurements of gene expression and chromatin accessibility, finding evidence for a shared ancestry between delta and epsilon cells. Moscot is available as an easy-to-use, open-source python package with extensive documentation at https://moscot-tools.org.

Recently Venumadhav et al. proposed a new pipeline to analyze LIGO–Virgo Collaboration’s O1–O2 data, and discovered eight new binary black hole (BBH) mergers, including one with a high effective spin, . This discovery helps to clarify the origin of the observed BBHs and the dynamical capture versus field binaries debate. Using a tide-wind model that characterizes the late phases of binary evolution and captures the essence of field binary spin evolution, we show that the observed distribution favors this model over capture. However, given the current limited sample size, capture scenarios (isotropic models) cannot be ruled out. Observations of roughly 100 merges will enable us to distinguish between the different formation scenarios. However, if as expected, both formation channels operate, it may be difficult to resolve their exact fraction.

The 6 th Conference of the Polish Society on Relativity (POTOR-6) was organized by the Szczecin Cosmology Group on behalf of the Polish Society on Relativity. In the latest years, this series of events has become a regular annual meeting for the Polish scientists active in the field of gravity and related topics, including cosmology, given that the conferences are open to international invited speakers and participants. It was a great honor for the Szczecin Cosmology Group to be the host of the 2019 event, due to the prestige of the Polish Society on Relativity, and because it sounded like a further recognition to the efforts put by its members to make Szczecin an important node of the scientific studies in relativity and cosmology in Poland. The Szczecin Cosmology Group was initially established at the University of Szczecin in the early 1990s because of the initiative of Prof. Mariusz P. Dąbrowski with the support of late Prof. Jerzy Stelmach and now emeritus Prof. Janusz Garecki. Since then, it has grown up till the present team, made of three permanent members (in addition to Prof. Dabrowski, two recently appointed Associate Professors, Adam Balcerzak and Vincenzo Salzano), one senior member (Dr. Tomasz Denkiewicz), and five ongoing Ph.D. students, from all over Europe, making it a very open and cosmopolitan team. Apart from a productive research activity finalized in a multiple series of scientific papers published in the most renowned journals, the group has also led the organization of several international conferences. In particular, the group has organized four topical COSMOFUN (COSMOlogy and FUNdamental interactions) events: COSMOFUN 2005 (Pomeranian Workshop in FUNdamental COSMOlogy), GRASSCOSMOFUN'09 (GRASSmanian Conference in FUNdamental COSMOlogy), MULTICOSMOFUN 2012 (MULTIverse and FUNdamental COSMOlogy), and VARCOSMOFUN'16 (VARying constants and FUNdamental COSMOlogy). The POTOR-6 conference has covered all aspects of classical relativity as well as related fields including contemporary theories of unification and quantum gravity. A list of topics included: theoretical and numerical relativity; black holes: classical and quantum aspects; relativistic cosmology; early universe, dark matter, and dark energy problems; gravitational waves; approaches to quantum gravity; quantum cosmology; relation of relativity and superstring theory. This provided the conference was able to cover a diverse area of topics which were rooted in relativity.

Elucidating underlying biological processes in single-cell data is an ongoing challenge and the number of methods that recapitulate dominant signals in such data has increased significantly. However, cellular populations encode multiple biological attributes, related to their spatial configuration, temporal trajectories, cell-cell interactions, and responses to environmental cues, which may be overshadowed by the dominant signal and thus much harder to recover. To approach this task, we developed SiFT (SIgnal FilTering), a method for filtering biological signals in single-cell data, thus uncovering underlying processes of interest. Utilizing existing prior knowledge and reconstruction tools for a specific biological signal, such as spatial structure, SiFT filters the signal and uncovers additional biological attributes. SiFT is applicable to a wide range of tasks, from the removal of unwanted variation in the data as a pre-processing step to revealing hidden biological structures. Applied for pre-processing, SiFT outperforms state-of-the-art methods for the removal of nuisance signals and cell cycle effects. To recover underlying biological structure, we use existing prior knowledge regarding liver zonation to filter the spatial signal from single-cell liver data thereby enhancing the temporal circadian signal the cells are encoding. Lastly, we showcase the applicability of SiFT in the case-control setting for studying COVID-19 disease. Filtering the healthy signal, based on reference samples from healthy donors, exposes disease-related dynamics in COVID-19 data and highlights disease informative cells and their underlying disease response pathways.