Biology subjects

Moshkov, N.

Publications and source records attributed to Moshkov, N..

9 recordsLinked to original sources

bioRxiv2025

Direct, high-throughput linking of single cell imaging and gene expression

Understanding sequence-phenotype relationships in biology requires large-scale datasets that provide one-to-one mapping of sequence to phenotype. Here, we address this challenge by introducing multimodal droplet barcoding, enabling individual microfluidic droplets to be tracked across both optical and sequencing measurements. We demonstrate the power of this approach through multimodal single-cell analysis. We pair imaging with gene expression profiling of individual cells, identifying genes that correlate with phenotypic features.

genomics↗

bioRxiv2025

A Morpho-Proteomic Atlas of Mitosis at Sub-Minute Resolution

Precise spatiotemporal protein organization is critical for fundamental biological processes including cell division1,2. Indeed, aberrant mitosis and mitotic factors are involved in diverse diseases, including various cancers3,4, Alzheimers disease5, and rare diseases6. During mitosis, complex spatial rearrangements and regulation ensure the accurate separation of replicated sister chromatids to produce genetically identical daughter cells7-9. Previous studies employed high-throughput methodologies to follow specific proteins during mitosis10-15. Still a temporally refined systems-level approach capable of monitoring morphological and proteomic changes throughout mitosis has been lacking. Here, we achieved unprecedented resolution by phenotypically decomposing mitosis into 40 subsections of a regression plane for proteomic analysis using deep learning and regression techniques. Our deep visual proteomics (DVP) workflow16, revealed rapid, dynamic proteomic changes throughout mitosis. We quantified 4,350 proteins with high confidence, demonstrating that 147 show significant dynamic abundance changes during mitotic progression. Clustering revealed coordinated patterns of protein regulation, while network analysis uncovered tight regulation of core cell cycle proteins and a link between cell cycle and cancer-linked mutations. Immunofluorescence validated abundance changes and linked previously uncharacterised proteins, like C19orf53, to mitosis. To facilitate data navigation, we developed Mito-Omix, a user-friendly online platform that integrates intricate morphological and molecular data. Our morphological and proteomic dataset spans mitosis at high resolution, providing a rich resource for understanding healthy and aberrant cell division.

cell biology↗

bioRxiv2024

Morphological map of under- and over-expression of genes in human cells

Cell Painting images offer valuable insights into a cells state and enable many biological applications, but publicly available arrayed datasets only include hundreds of genes perturbed. The JUMP (Joint Undertaking in Morphological Profiling) Cell Painting Consortium perturbed roughly 75% of the protein-coding genome in human U-2 OS cells, generating a rich resource of single-cell images and extracted features. These profiles capture the phenotypic impacts of perturbing 15,243 human genes, including overexpressing 12,609 genes (using open reading frames, ORFs) and knocking out 7,975 genes (using CRISPR-Cas9). We mitigated technical artifacts by rigorously evaluating data processing options and validated the datasets robustness and biological relevance. Analysis of phenotypic profiles revealed novel gene clusters and functional relationships, including those associated with mitochondrial function, cancer, and neural processes. The JUMP Cell Painting genetic dataset is a valuable resource for exploring gene relationships and uncovering novel functions.

bioinformatics↗

bioRxiv2023

Unbiased single-cell morphology with self-supervised vision transformers

Accurately quantifying cellular morphology at scale could substantially empower existing single-cell approaches. However, measuring cell morphology remains an active field of research, which has inspired multiple computer vision algorithms over the years. Here, we show that DINO, a vision-transformer based, self-supervised algorithm, has a remarkable ability for learning rich representations of cellular morphology without manual annotations or any other type of supervision. We evaluate DINO on a wide variety of tasks across three publicly available imaging datasets of diverse specifications and biological focus. We find that DINO encodes meaningful features of cellular morphology at multiple scales, from subcellular and single-cell resolution, to multi-cellular and aggregated experimental groups. Importantly, DINO successfully uncovers a hierarchy of biological and technical factors of variation in imaging datasets. The results show that DINO can support the study of unknown biological variation, including single-cell heterogeneity and relationships between samples, making it an excellent tool for image-based biological discovery.

bioinformatics↗

bioRxiv2022

Learning representations for image-based profiling of perturbations

Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient and powerful way of studying cell biology, and requires computational methods for transforming images into quantitative data that highlight phenotypic outcomes. Here, we present an optimized strategy for learning representations of treatment effects from high-throughput imaging data, which follows a causal framework for interpreting results and guiding performance improvements. We use weakly supervised learning (WSL) for modeling associations between images and treatments, and show that it encodes both confounding factors and phenotypic features in the learned representation. To facilitate their separation, we constructed a large training dataset with Cell Painting images from five different studies to maximize experimental diversity, following insights from our causal analysis. Training a WSL model with this dataset successfully improves downstream performance, and produces a reusable convolutional network for image-based profiling, which we call Cell Painting CNN-1. We conducted a comprehensive evaluation of our strategy on three publicly available Cell Painting datasets, discovering that representations obtained by the Cell Painting CNN-1 can improve performance in downstream analysis for biological matching up to 30% with respect to classical features, while also being more computationally efficient.

bioinformatics↗

bioRxiv2020

SpheroidPicker: An Automated 3D cell culture manipulator robot using deep learning

Recent statistics report that more than 3.7 million new cases of cancer occur in Europe yearly, and the disease accounts for approximately 20 % of all deaths. High-throughput screening of cancer cell cultures has dominated the search for novel, effective anticancer therapies in the past decades. Recently, ex vivo 3D cell cultures from the patients own cancer cells have gained importance. We recently evaluated the major advancements and needs of the 3D cell cultures screening field, and we concluded that strictly standardized sample preparation is the most desired development. Here we propose an artificial intelligence-guided low-cost 3D cell culture delivery system. It consists of a light microscope, a micromanipulator, a syringe pump, and a controller computer. The system performs morphology-based feature analysis on spheroids and transfers the most appropriate ones between various sample holders. It can select the samples from standard sample holders, including Petri dishes and microwell plates, and then transfer them to a variety of holders up to 384 well plates. The device performs reliable semi- and fully automated spheroid transfer. This results in highly controlled experimental conditions and eliminates non-trivial side effects of sample variability that is a key aspect towards next-generation precision medicine.

bioengineering↗

bioRxiv2020

Local Ancestry Prediction with PyLAE

SummaryWe developed PyLAE - a new tool for determining local ancestry along a genome using whole-genome sequencing data or high-density genotyping experiments. PyLAE can process an arbitrarily large number of ancestral populations (with or without an informative prior). Since PyLAE does not involve estimation of many parameters, it can process thousands of genomes within a day. Computational efficiency, straightforward presentation of results, and an ease of installation makes PyLAE a useful tool to study admixed populations. Availability and implementationThe source code and installation manual are available at https://github.com/smetam/pylae.

bioinformatics↗

bioRxiv2019

Diverse genetic origins of medieval steppe nomad conquerors

Over millennia, steppe nomadic tribes raided and sometimes overran settled Eurasian civilizations. Most polities formed by steppe nomads were ephemeral, making it difficult to ascertain their genetic roots or what present-day populations, if any, have descended from them. Exceptionally, the Khazar Khaganate controlled the trade artery between the Black and Caspian Seas in VIII-IX centuries, acting as one of the major conduits between East and West. However, the genetic identity of the ruling elite within the polyglot and polyethnic Khaganate has been a much-debated mystery; a controversial hypothesis posits that post-conversion to Judaism the Khazars gave rise to modern Ashkenazim. We analyzed whole-genome sequences of eight men and one woman buried within the distinctive kurgans of the Khazar upper (warrior) class. After comparing them with reference panels of present-day Eurasian and Iron Age populations, we found that the Khazar political organization relied on a polyethnic elite. It was predominantly descended from Central Asian tribes but incorporated genetic admixture from populations conquered by Khazars. Thus, the Khazar ruling class was likely relatively small and able to maintain a genetic identity distinct from their subjugated populations over the course of centuries. Yet, men of mixed ancestry could also rise into the warrior class, possibly providing troop numbers necessary to maintain control of their large territory. However, when the Khaganate collapsed it left few persistent genetic traces in Europe. Our data confirm the Turkic roots of the Khazars, but also highlight their ethnic diversity and some integration of conquered populations.View Full Text

paleontology↗

bioRxiv2019

Test-time augmentation for deep learning-based cell segmentation on microscopy images

Recent advancements in deep learning have revolutionized the way microscopy images of cells are processed. Deep learning network architectures have a large number of parameters, thus, in order to reach high accuracy, they require massive amount of annotated data. A common way of improving accuracy builds on the artificial increase of the training set by using different augmentation techniques. A less common way relies on test-time augmentation (TTA) which yields transformed versions of the image for prediction and the results are merged. In this paper we describe incorporating the test-time argumentation prediction method into two major segmentation approaches used in the single-cell analysis of microscopy images, namely semantic segmentation using U-Net and instance segmentation using Mask R-CNN models. Our findings show that even using only simple test-time augmentations, such as rotation or flipping and proper merging methods, will result in significant improvement of prediction accuracy. We utilized images of tissue and cell cultures from the Data Science Bowl (DSB) 2018 nuclei segmentation competition and other sources. Additionally, boosting the highest-scoring method of the DSB with TTA, we could further improve and our method has reached an ever-best score at the DSB.

bioinformatics↗