Biology subjects

Kalafut, N. C.

Publications and source records attributed to Kalafut, N. C..

5 recordsLinked to original sources

bioRxiv2025

Inferring virtual cell environments using multi-agent reinforcement learning

1Single cells interact continuously to form a cell environment that drives key biological processes. Cells and cell environments are highly dynamic across time and space, fundamentally governed by molecular mechanisms, such as gene expression. Recent sequencing techniques measure single-cell-level gene expression under specific conditions, either temporally or spatially. Using these datasets, emerging works, such as virtual cells, can learn biologically useful representations of individual cells. However, these representations are typically static and overlook the underlying cell environment and its dynamics. To address this, we developed CellTRIP, a multi-agent reinforcement learning method that infers a virtual cell environment to simulate the cell dynamics and interactions underlying given single-cell data. Specifically, cells are modeled as individual agents with dynamic interactions, which can be learned through self-attention mechanisms via reinforcement learning. CellTRIP also applies novel truncated reward boot-strapping and adaptive input rescaling to stabilize training. We can in-silico manipulate any combination of cells and genes in our learned virtual cell environment, predict spatial and/or temporal cell changes, and prioritize corresponding genes at the single-cell level. We applied and benchmarked CellTRIP on various simulated and real gene expression datasets, including recapitulating cellular dynamic processes simulated by gene regulatory networks and stochastic models, imputing spatial organization of mouse cortical cells, predicting developmental gene expression changes after drug treatment in cancer cells, and spatiotemporal reconstruction of Drosophila embryonic development, demonstrating its outperformance and broad applicability. Interactive manipulation of those virtual cell environments, including in-silico perturbation, can prioritize spatial and developmental genes for single-cell-level changes, enabling the generation of new insights into cell dynamics over time and space. CellTRIP is open source as a general tool and available at github.com/daifengwanglab/CellTRIP.

bioinformatics↗

bioRxiv2024

NeuroTD: A Time-Frequency Based Multimodal Learning Approach to Analyze Time Delays in Neural Activities

Studying the temporal dynamics of neural activities is essential for understanding how neurons function. These dynamics often involve temporal delays between neurons that vary over time, revealing both their functions and how they interact within circuits. Recent techniques such as Neuropixels, depth electrodes, and Patch-seq enable time-series recordings of neural activity at various scales, ranging from single neurons to large populations. However, inferring such time-varying delays remains challenging due to noise, high sampling rates, and complex temporal patterns. To address these challenges, we developed NeuroTD, a novel computational approach based on sliding windows to align time-series datasets and infer time-varying delays. Particularly, NeuroTD integrates adaptive window-size tuning to obtain optimal and robust delay estimates. We first benchmarked NeuroTD in simulation studies, demonstrating its robustness and outperformance. Then we applied it to two emerging real-world datasets: (i) intracranial multi-channel electrophysiological recordings from depth electrodes across medial temporal lobe regions in humans, showing that hippocampal signals recorded via depth electrodes exhibited consistently significantly longer time delays than other regions during working memory tasks, and (ii) Patch-seq data in the mouse motor cortex, revealing intrinsic electro-physiological time-delays of excitatory neurons correlated with gene expression and highlighting pathways related to ion transport and neuronal excitability. Finally, NeuroTD is open-source and available at https://github.com/daifengwanglab/NeuroTD for general use.

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bioRxiv2023

MANGEM - a web app for Multimodal Analysis of Neuronal Gene expression, Electrophysiology and Morphology

Single-cell techniques have enabled the acquisition of multi-modal data, particularly for neurons, to characterize cellular functions. Patch-seq, for example, combines patch-clamp recording, cell imaging, and single-cell RNA-seq to obtain electrophysiology, morphology, and gene expression data from a single neuron. While these multi-modal data offer potential insights into neuronal functions, they can be heterogeneous and noisy. To address this, machine-learning methods have been used to align cells from different modalities onto a low-dimensional latent space, revealing multi-modal cell clusters. However, the use of those methods can be challenging for biologists and neuroscientists without computational expertise and also requires suitable computing infrastructure for computationally expensive methods. To address these issues, we developed a cloud-based web application, MANGEM (Multimodal Analysis of Neuronal Gene expression, Electrophysiology, and Morphology) at https://ctc.waisman.wisc.edu/mangem. MANGEM provides a step-by-step accessible and user-friendly interface to machine-learning alignment methods of neuronal multi-modal data while enabling real-time visualization of characteristics of raw and aligned cells. It can be run asynchronously for large-scale data alignment, provides users with various downstream analyses of aligned cells and visualizes the analytic results such as identifying multi-modal cell clusters of cells and detecting correlated genes with electrophysiological and morphological features. We demonstrated the usage of MANGEM by aligning Patch-seq multimodal data of neuronal cells in the mouse visual cortex. Author SummaryThe human brain is made up of billions of tiny cells called neurons, each with their own important job. Scientists are now able to study individual neurons in more detail than ever before using new advanced techniques. They can look at different data of individual neurons like how genes are being used (gene expression), how the neuron responds to electrical signals (electrophysiology), and what it looks like (morphology). By combining all of this information, they can start to group similar neurons together and figure out what they do. However, due to the data complexity, this process can be very complicated and hard for researchers without sufficient computational skills. To address this, we developed a web app, MANGEM (Multimodal Analysis of Neuronal Gene Expression, Electrophysiology, and Morphology). It lets scientists upload their data and select emerging machine-learning approaches to find groups of similar neurons. It also makes interactive visualizations to help them explore the characteristics of neuron groups and understand what they do.

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bioRxiv2022

JAMIE: Joint Variational Autoencoders for Multi-Modal Imputation and Embedding

Single-cell multimodal datasets have measured various characteristics of individual cells, enabling a deep understanding of cellular and molecular mechanisms. However, multimodal data generation remains costly and challenging, and missing modalities happen frequently. Recently, machine learning approaches have been developed for data imputation but typically require fully matched multimodalities to learn common latent embeddings that potentially lack modality specificity. To address these issues, we developed an open-source machine learning model, Joint Variational Autoencoders for multimodal Imputation and Embedding (JAMIE). JAMIE takes single-cell multimodal data that can have partially matched samples across modalities. Variational autoencoders learn the latent embeddings of each modality. Then, embeddings from matched samples across modalities are aggregated to identify joint cross-modal latent embeddings before reconstruction. To perform cross-modal imputation, the latent embeddings of one modality can be used with the decoder of the other modality. For interpretability, Shapley values are used to prioritize input features for cross-modal imputation and known sample labels. We applied JAMIE to both simulation data and emerging single-cell multimodal data including gene expression, chromatin accessibility, and electrophysiology in human and mouse brains. JAMIE significantly outperforms existing state-of-the-art methods in general and prioritized multimodal features for imputation, providing potentially novel mechanistic insights at cellular resolution.

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bioRxiv2022

Brain and Organoid Manifold Alignment (BOMA), a machine learning framework for comparative gene expression analysis across brains and organoids

Organoids have become valuable models for understanding cellular and molecular mechanisms in human development including brains. However, whether developmental gene expression programs are preserved between human organoids and brains, especially in specific cell types, remains unclear. Importantly, there is a lack of effective computational approaches for comparative data analyses between organoids and developing humans. To address this, by considering the public data availability and research significance, we developed a machine learning framework, Brain and Organoid Manifold Alignment (BOMA) for comparative gene expression analysis of brains and organoids, to identify conserved and specific developmental trajectories as well as developmentally expressed genes and functions, especially at cellular resolution. BOMA first performs a global alignment and then uses manifold learning to locally refine the alignment, revealing conserved developmental trajectories between brains and organoids. Using BOMA, we found that human cortical organoids better align with certain brain cortical regions than other non-cortical regions, implying organoid-preserved developmental gene expression programs specific to brain regions. Additionally, our alignment of non-human primate and human brains reveals highly conserved gene expression around birth. Also, we integrated and analyzed developmental scRNA-seq data of human brains and organoids, showing conserved and specific cell trajectories and clusters. Further identification of expressed genes of such clusters and enrichment analyses reveal brain- or organoid-specific developmental functions and pathways. Finally, we experimentally validated important specific expressed genes using immunofluorescence. BOMA is open-source available as a web tool for general community use.

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