Mapping cells through time and space with moscot.

Single-cell genomic technologies enable the multimodal profiling of millions of cells across temporal and spatial dimensions. However, experimental limitations hinder the comprehensive measurement of cells under native temporal dynamics and in their native spatial tissue niche. Optimal transport has emerged as a powerful tool to address these constraints and has facilitated the recovery of the original cellular context.

Interpreting single-cell and spatial omics data using deep neural network training dynamics.

Single-cell and spatial omics datasets can be organized and interpreted by annotating single cells to distinct types, states, locations or phenotypes. However, cell annotations are inherently ambiguous, as discrete labels with subjective interpretations are assigned to heterogeneous cell populations on the basis of noisy, sparse and high-dimensional data. Here we developed Annotatability, a framework for identifying annotation mismatches and characterizing biological data structure by monitoring the dynamics and difficulty of training a deep neural network over such annotated data.

Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery.

Over the past decade, single-cell genomics technologies have allowed scalable profiling of cell-type-specific features, which has substantially increased our ability to study cellular diversity and transcriptional programs in heterogeneous tissues. Yet our understanding of mechanisms of gene regulation or the rules that govern interactions between cell types is still limited. The advent of new computational pipelines and technologies, such as single-cell epigenomics and spatially resolved transcriptomics, has created opportunities to explore two new axes of biological variation: cell-intrinsic regulation of cell states and expression programs and interactions between cells.

Mapping lineage-traced cells across time points with moslin.

Simultaneous profiling of single-cell gene expression and lineage history holds enormous potential for studying cellular decision-making. Recent computational approaches combine both modalities into cellular trajectories; however, they cannot make use of all available lineage information in destructive time-series experiments. Here, we present moslin, a Gromov-Wasserstein-based model to couple cellular profiles across time points based on lineage and gene expression information.

Optimal sequencing budget allocation for trajectory reconstruction of single cells.

Background: Charting cellular trajectories over gene expression is key to understanding dynamic cellular processes and their underlying mechanisms. While advances in single-cell RNA-sequencing technologies and computational methods have pushed forward the recovery of such trajectories, trajectory inference remains a challenge due to the noisy, sparse, and high-dimensional nature of single-cell data. This challenge can be alleviated by increasing either the number of cells sampled along the trajectory (breadth) or the sequencing depth, i.

SiFT: uncovering hidden biological processes by probabilistic filtering of single-cell data.

Cellular populations simultaneously encode multiple biological attributes, including spatial configuration, temporal trajectories, and cell-cell interactions. Some of these signals may be overshadowed by others and harder to recover, despite the great progress made to computationally reconstruct biological processes from single-cell data. To address this, we present SiFT, a kernel-based projection method for filtering biological signals in single-cell data, thus uncovering underlying biological processes.

Disentanglement of single-cell data with biolord.

Biolord is a deep generative method for disentangling single-cell multi-omic data to known and unknown attributes, including spatial, temporal and disease states, used to reveal the decoupled biological signatures over diverse single-cell modalities and biological systems. By virtually shifting cells across states, biolord generates experimentally inaccessible samples, outperforming state-of-the-art methods in predictions of cellular response to unseen drugs and genetic perturbations. Biolord is available at https://github.

The 2023 wearable photoplethysmography roadmap.

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making.

Robust reconstruction of single-cell RNA-seq data with iterative gene weight updates.

Motivation: Single-cell RNA-sequencing technologies have greatly enhanced our understanding of heterogeneous cell populations and underlying regulatory processes. However, structural (spatial or temporal) relations between cells are lost during cell dissociation. These relations are crucial for identifying associated biological processes.

scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching.

Single-cell RNA sequencing has been instrumental in uncovering cellular spatiotemporal context. This task is challenging as cells simultaneously encode multiple, potentially cross-interfering, biological signals. Here we propose scPrisma, a spectral computational method that uses topological priors to decouple, enhance and filter different classes of biological processes in single-cell data, such as periodic and linear signals.

TACCO unifies annotation transfer and decomposition of cell identities for single-cell and spatial omics.

Transferring annotations of single-cell-, spatial- and multi-omics data is often challenging owing both to technical limitations, such as low spatial resolution or high dropout fraction, and to biological variations, such as continuous spectra of cell states. Based on the concept that these data are often best described as continuous mixtures of cells or molecules, we present a computational framework for the transfer of annotations to cells and their combinations (TACCO), which consists of an optimal transport model extended with different wrappers to annotate a wide variety of data. We apply TACCO to identify cell types and states, decipher spatiomolecular tissue structure at the cell and molecular level and resolve differentiation trajectories using synthetic and biological datasets.

Overestimation of Oxygen Saturation Measured by Pulse Oximetry in Hypoxemia. Part 1: Effect of Optical Pathlengths-Ratio Increase.

On average, arterial oxygen saturation measured by pulse oximetry (SpO) is higher in hypoxemia than the true oxygen saturation measured invasively (SaO), thereby increasing the risk of occult hypoxemia. In the current article, measurements of SpO on 17 cyanotic newborns were performed by means of a Nellcor pulse oximeter (POx), based on light with two wavelengths in the red and infrared regions (660 and 900 nm), and by means of a novel POx, based on two wavelengths in the infrared region (761 and 820 nm). The SpO readings from the two POxs showed higher values than the invasive SaO readings, and the disparity increased with decreasing SaO.

Feasibility of Precision Medicine in Hypertension Management-Scope and Technological Aspects.

Personalized management of diseases by considering relevant patient features enables optimal treatment, instead of management according to an average patient. Precision management of hypertension is important, because both susceptibility to complications and response to treatment vary between individuals. While the use of genomic and proteomic personal features for widespread precision hypertension management is not practical, other features, such as age, ethnicity, and cardiovascular diseases, have been utilized in guidelines for hypertension management.

Programming cell growth into different cluster shapes using diffusible signals.

Advances in genetic engineering technologies have allowed the construction of artificial genetic circuits, which have been used to generate spatial patterns of differential gene expression. However, the question of how cells can be programmed, and how complex the rules need to be, to achieve a desired tissue morphology has received less attention. Here, we address these questions by developing a mathematical model to study how cells can collectively grow into clusters with different structural morphologies by secreting diffusible signals that can influence cellular growth rates.

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

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.

NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport.

Single-cell RNA-sequencing (scRNA-seq) technologies have revolutionized modern biomedical sciences. A fundamental challenge is to incorporate spatial information to study tissue organization and spatial gene expression patterns. Here, we describe a detailed protocol for using novoSpaRc, a computational framework that probabilistically assigns cells to tissue locations.

Revealing lineage-related signals in single-cell gene expression using random matrix theory.

Gene expression profiles of a cellular population, generated by single-cell RNA sequencing, contains rich information about biological state, including cell type, cell cycle phase, gene regulatory patterns, and location within the tissue of origin. A major challenge is to disentangle information about these different biological states from each other, including distinguishing from cell lineage, since the correlation of cellular expression patterns is necessarily contaminated by ancestry. Here, we use a recent advance in random matrix theory, discovered in the context of protein phylogeny, to identify differentiation or ancestry-related processes in single-cell data.

ChIP-seq of plasma cell-free nucleosomes identifies gene expression programs of the cells of origin.

Cell-free DNA (cfDNA) in human plasma provides access to molecular information about the pathological processes in the organs or tumors from which it originates. These DNA fragments are derived from fragmented chromatin in dying cells and retain some of the cell-of-origin histone modifications. In this study, we applied chromatin immunoprecipitation of cell-free nucleosomes carrying active chromatin modifications followed by sequencing (cfChIP-seq) to 268 human samples.

The Various Oximetric Techniques Used for the Evaluation of Blood Oxygenation.

Adequate oxygen delivery to a tissue depends on sufficient oxygen content in arterial blood and blood flow to the tissue. Oximetry is a technique for the assessment of blood oxygenation by measurements of light transmission through the blood, which is based on the different absorption spectra of oxygenated and deoxygenated hemoglobin. Oxygen saturation in arterial blood provides information on the adequacy of respiration and is routinely measured in clinical settings, utilizing pulse oximetry.

Selective flexible packaging pathways of the segmented genome of influenza A virus.

The genome of influenza A viruses (IAV) is encoded in eight distinct viral ribonucleoproteins (vRNPs) that consist of negative sense viral RNA (vRNA) covered by the IAV nucleoprotein. Previous studies strongly support a selective packaging model by which vRNP segments are bundling to an octameric complex, which is integrated into budding virions. However, the pathway(s) generating a complete genome bundle is not known.