Biology subjects

Sadria, M.

Publications and source records attributed to Sadria, M..

12 recordsLinked to original sources

bioRxiv2026

HLiCA: An integrated cell atlas of the healthy human liver

The human liver is composed of a heterogeneous mix of cell types. How these distinct populations contribute individually and collectively to liver function remains poorly understood. Although single-cell technologies have advanced our understanding of liver biology, individual studies have often been limited by small donor cohorts and inconsistent cell type annotations. Integrating multiple datasets can overcome these challenges and better capture biological variability. We present the Human Liver Cell Atlas (HLiCA), an integrated reference of non-disease liver cells assembled from eight datasets across six research centers, encompassing more than 525,000 cells from 110 donors. Developed in collaboration with the Human Cell Atlas Liver Bionetwork, the HLiCA incorporates expert-curated cell annotations refined through community feedback and dedicated cell type annotation meetings. The HLiCA classifies cells into six lineages and expands the cell type resolution to include 47 distinct cell types. Starting from raw sequencing reads, we realigned all data and performed rigorous benchmarking to ensure robust integration across technical and biological variables. Genetic ancestry was inferred for all samples to evaluate the range of ancestral backgrounds represented in the atlas. The expanded cell type annotation enabled identification of previously unrecognized liver cell types, including NRXN1+ stromal cells. Their presence was validated using spatial transcriptomics, which localized NRXN1+ stromal cells to periportal regions. With the number of donors included in the HLiCA we were able to examine cell type specific associations with demographic covariates. In hepatocytes, drug metabolism genes showed differential expression between sexes, and in cholangiocytes, mucus-production genes varied with age. As the largest and most genetically diverse human liver cell atlas to date, the HLiCA provides a comprehensive, well-annotated reference for the field, annotated by expert consensus. This resource will enable deeper interrogation of liver cellular diversity, architecture, and function in the healthy human liver and serve as a reference to understand changes that occur with disease.

genomics↗

bioRxiv2026

LineageSim: A Single-Cell Lineage Simulator with Fate-Aware Gene Expression

Single-cell lineage data paired with gene expression are critical for developing computational methods in developmental biology. Since experimental lineage tracing is often technically limited, robust simulations are necessary to provide the ground truth for rigorous validation. However, existing simulators generate largely Markovian gene expression, failing to encode the fate bias observed in real biological systems, where progenitor states exhibit early signatures of future commitment. Consequently, they cannot support the training and evaluation of computational methods that model long-range temporal dependencies. We present LineageSim, a generative framework that introduces fate-aware gene expression, where progenitor states carry latent signals of their descendants terminal fates. This framework establishes a new class of benchmarks for cell fate prediction algorithms. We validate the presence of these temporal signals by training a logistic regression baseline, which achieves 68.3% balanced accuracy. This confirms that the generated data contain subtle but recoverable fate information, in contrast to existing simulators, where such predictive signals are systematically absent.

bioinformatics↗

bioRxiv2024

Discovering Governing Equations of Biological Systems through Representation Learning and Sparse Model Discovery

Understanding the governing rules of complex biological systems remains a significant challenge due to the nonlinear, high-dimensional nature of biological data. In this study, we present CLERA, a novel end-to-end computational framework designed to uncover parsimonious dynamical models and identify active gene programs from single-cell RNA sequencing data. By integrating a supervised autoencoder architecture with Sparse Identification of Nonlinear Dynamics, CLERA leverages prior knowledge to simultaneously extract related low-dimensional embeddings and uncovers the underlying dynamical systems that drive the processes. Through the analysis of both synthetic and biological datasets, CLERA demonstrates robust performance in reconstructing gene expression dynamics, identifying key regulatory genes, and capturing temporal patterns across distinct cell types. CLERAs ability to generate dynamic interaction networks, combined with network rewiring using Personalized PageRank to highlight central genes and active gene programs, offers new insights into the complex regulatory mechanisms underlying cellular processes.

bioinformatics↗

bioRxiv2024

Direct lineage conversion of postnatal mouse cortical astrocytes to oligodendrocyte lineage cells

Oligodendrocyte lineage cells (OLCs) are lost in many CNS diseases. Here, we investigate the generation of new OLCs via ectopic expression of Sox10, Olig2 or Nkx6.2 in mouse postnatal astrocytes. Using stringent analyses including, Aldh1l1-astrocyte fate mapping and live cell imaging we confirm that Sox10 and Olig2, but not Nkx6.2, convert Aldh1l1pos astrocytes to induced OLCs (iOLCs). With single cell RNA sequencing (scRNA-seq) we uncover the molecular signatures of iOLCs. Transcriptomic analysis of Sox10- and control cultures over time reveals a clear trajectory from astrocytes to iOLCs. Finally, perturbation models CellOracle and Fatecode support the idea that Sox10 drives cells towards a terminal iOLC fate. Altogether, this multidimensional analysis shows bonafide conversion of astrocytes to iOLCs using Sox10 or Olig2 and provides a foundation for astrocyte DLR strategies to promote OLC repair.

developmental biology↗

bioRxiv2024

Deep Lineage: Single-Cell Lineage Tracing and Fate Inference Using Deep Learning

Recent advances in single-cell RNA-sequencing and lineage tracing techniques have provided valuable insights into the temporal changes in gene expression during development, tumour progression, and disease onset. However, there are few computational methods available to analyze this information to help understand multicellular dynamics. We introduce Deep Lineage, a novel deep-learning method for analyzing time-series single-cell RNA-sequencing with matched lineage-tracing data. Our method accurately predicts early cell fate biases and gene expression profiles at different time points within a clone, surpassing current state-of-the-art methods in fate prediction accuracy. Additionally, through in silico perturbations in cellular reprogramming and hematopoiesis development data, we show that Deep Lineage can accurately model dynamic multicellular responses while identifying key genes and pathways associated with cell fate determination.

bioinformatics↗

bioRxiv2024

FateNet: an integration of dynamical systems and deep learning for cell fate prediction

Understanding cellular decision-making, particularly its timing and impact on the biological system such as tissue health and function, is a fundamental challenge in biology and medicine. Existing methods for inferring fate decisions and cellular state dynamics from single-cell RNA sequencing data lack precision regarding decision points and broader tissue implications. Addressing this gap, we present FateNet, a computational approach integrating dynamical systems theory and deep learning to probe the cell decision-making process using scRNA-seq data. By leveraging information about normal forms and scaling behavior near tipping pointscommon to many dynamical systems, FateNet accurately predicts cell decision occurrence and offers qualitative insights into the new state of the biological system. Also, through in-silico perturbation experiments, FateNet identifies key genes and pathways governing the differentiation process in hematopoiesis. Validated using different scRNA-seq data, FateNet emerges as a user-friendly and valuable tool for predicting critical points in biological processes, providing insights into complex trajectories.

bioinformatics↗

bioRxiv2023

Adversarial training improves model interpretability in single-cell RNA-seq analysis

For predictive computational models to be considered reliable in crucial areas such as biology and medicine, it is essential for them to be accurate, robust, and interpretable. A sufficiently robust model should not have its output affected significantly by a slight change in the input. Also, these models should be able to explain how a decision is made. Efforts have been made to improve the robustness and interpretability of these models as independent challenges, however, the effect of robustness and interpretability on each other is poorly understood. Here, we show that predicting cell type based on single-cell RNA-seq data is more robust by adversarially training a deep learning model. Surprisingly, we find this also leads to improved model interpretability, as measured by identifying genes important for classification. We believe that adversarial training will be generally useful to improve deep learning robustness and interpretability, thereby facilitating biological discovery.

bioinformatics↗

bioRxiv2023

The Power of Two: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis

Discovering a lower-dimensional embedding of single-cell data can greatly improve downstream analysis. The embedding should encapsulate both the high-level semantics and low-level variations in order to be meaningful and interpretable. Although current generative models have been used to learn such a low-dimensional representation, they have several limitations. Here, we introduce scVAEDer, a scalable deep-learning model that combines the power of variational autoencoders and deep diffusion models to learn a meaningful representation which can capture both global semantics and local variations in the data. By using the learned embedding, we show that scVAEDer can generate novel scRNA-seq data, predict the effect of the perturbation on various cell types, identify changes in gene expression during dedifferentiation, and detect master regulators in a biological process.

bioinformatics↗

bioRxiv2022

Fatecode: Cell fate regulator prediction using classification autoencoder perturbation

Cell reprogramming, which guides the conversion between cell states, is a promising technology for tissue repair and regeneration. Typically, a group of key genes, or master regulators, are manipulated to control cell fate, with the ultimate goal of accelerating recovery from diseases or injuries. Of importance is the ability to correctly identify the master regulators from single-cell transcriptomics datasets. To accomplish that goal, we propose Fatecode, a computational method that combines in silico perturbation experiments with cell trajectory modeling using deep learning to predict master regulators and key pathways controlling cell fate. Fatecode uses only scRNA-seq data from wild-type samples to learn and predict how cell type distribution changes following a perturbation. We assessed Fatecodes performance using simulations from a mechanistic gene regulatory network model and diverse gene expression profiles covering blood and brain development. Our results suggest that Fatecode can detect known master regulators of cell fate from single-cell transcriptomics datasets. That capability points to Fatecodes potential in accelerating the discovery of cell fate regulators that can be used to engineer and grow cells for therapeutic use in regenerative medicine applications.

systems biology↗

bioRxiv2022

Determinants of natriuretic, diuretic, and kaliuretic effects of diuretics: Sex and administration time

Sex differences in renal function and blood pressure have been widely described across many species. Blood pressure dips during sleep and peaks in the early morning. Similarly, glomerular filtration rate, filtered electrolyte loads, urine volume, and urinary excretion all exhibit notable diurnal rhythms, which reflect, in part, the regulation of renal transporter proteins by circadian clock genes. That regulation is sexually dimorphic; as such, sex and time-of-day are not two independent regulators of kidney function and blood pressure. The objective of this study is to assess the effect of sex and administration time on the natriuretic and diuretic effects of loop, thiazide, and K+-sparing diuretics, which are common treatment for hypertension. Loop diuretics inhibit NKCC2 on the apical membrane of the thick ascending limb, thiazide diuretics inhibit NCC on the distal convoluted tubule, and K+-sparing diuretics inhibit ENaC on the connecting tubule and collecting duct. We simulate Na+ transporter inhibition using our sex- and time-of-day-specific computational models of mouse kidney function. Simulations results highlight significant sex and time-of day differences in drug response. Loop diuretics induce larger natriuretic and diuretic effects during the active phase. The natriuretic and diuretic effects of thiazide diuretics exhibit sex and time-of-day differences, whereas these effects of K+-sparing diuretics exhibit significant time-of-day difference in females only. Kaliuretic effect depends on the type of diuretics and time of administration. The present computational models can be a useful tool in chronotherapy, to tailor drug administration time to match the bodys diurnal rhythms to optimize the drug effect.

physiology↗

bioRxiv2021

The Mixed Blessing of AMPK Signaling in Cancer Treatments

Nutrient acquisition and metabolism pathways are altered in cancer cells to meet bioenergetic and biosynthetic demands. A major regulator of cellular metabolism and energy homeostasis, in normal and cancer cells, is AMP-activated protein kinase (AMPK). AMPK influences cell growth via its modulation of the mechanistic target of Rapamycin (mTOR) pathway, specifically, by inhibiting mTOR complex mTORC1, which facilitates cell proliferation, and by activating mTORC2 and cell survival. Given its conflicting roles, the effects of AMPK activation in cancer can be counter-intuitive. Prior to the establishment of cancer, AMPK acts as a tumor suppressor. However, following the onset of cancer, AMPK has been shown to either suppress or promote cancer, depending on cell type or state. To unravel the controversial roles of AMPK in cancer, we developed a computational model to simulate the effects of pharmacological maneuvers that target key metabolic signalling nodes, with specific focus on AMPK, mTORC, and their modulators. Model simulations clarify the competing effects and the roles of key metabolic signalling pathways in tumorigenesis, which may yield insights on innovative therapeutic strategies.

systems biology↗

bioRxiv2020

Interactions among mTORC, AMPK, and SIRT: A Computational Model for Cell Energy Balance and Metabolism

Key proteins such as mTORC, AMPK, and sirtuins are known to play an essential role in the management of metabolic stress and ageing mechanisms. An impairment in these mechanisms is commonly associated with cellular ageing and degenerative diseases. To understand the complex interactions of ageing{square}related signalling pathways and environmental signals, and the impacts on lifespan and health-span, we developed a computational model of ageing signalling pathways. The model includes (i) the insulin/IGF-1 pathway, which couples energy and nutrient abundance to the execution of cell growth and division, (ii) mTORC1 and amino acid sensors, (iii) the Preiss-Handler and salvage pathways, which regulate the metabolism of NAD+ and the NAD+-consuming factor SIRT1, (iv) the energy sensor AMPK, and (v) transcription factors FOXO and PGC-1. Key findings include the clinically important role of PRAS40, sestrin2, and diet in the treatment of cancers and other diseases, and a potential link between SIRT1-activating compounds and premature autophagy. The model can be used as an essential component to simulate gene manipulation, therapies (e.g., rapamycin and wortmannin), calorie restrictions, and chronic stress, and to assess their functional implications on longevity and ageing{square}related diseases. Author SummaryIn cellular ageing, mitochondrial function declines over time, which affects normal mechanisms of cells and organisms and leads to myriad of degenerative diseases and other health problems. To investigate the mechanisms that affect the ageing process, we focus on pathways that play a key role in the management of metabolic stress: the mTORC, AMPK, and sirtuins pathways. Our goal is to understand the complex interactions of ageing and metabolism related signalling pathways and environmental signals, and the impacts on lifespan and health-span. To accomplish that goal, we developed a computational model of signalling pathways related to ageing and metabolism. By conducting model simulations, we have unraveled the clinically important role of PRAS40, sestrin2, and diet in the treatment of cancers and other diseases, and a double-edged sword effect of SIRT1-activating compounds in their use as a health remedy. We view this model as an essential step towards a tool for studying metabolism, longevity, and ageing-related diseases. By extending the present model as appropriate, we can simulate gene manipulation, therapies (e.g., rapamycin and wortmannin), calorie restrictions, and chronic stress.

systems biology↗