Tempo: an unsupervised Bayesian algorithm for circadian phase inference in single-cell transcriptomics

Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

Across biological systems, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. While low-dimensional dynamics can be extracted using RNA velocity, these algorithms can be fragile and rely on heuristics lacking statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. To address these challenges, we introduce a Bayesian model of RNA velocity that couples velocity field and manifold estimation in a reformulated, unified framework, identifying the parameters of an explicit dynamical system. Focusing on the cell cycle, we implement VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validate its ability to infer cell cycle periods using live imaging. We also apply VeloCycle to reveal speed differences in regionally defined progenitors and Perturb-seq gene knockdowns. Overall, VeloCycle expands the single-cell RNA sequencing analysis toolkit with a modular and statistically consistent RNA velocity inference framework.

Detecting Rhythmic Gene Expression in Single-cell Transcriptomics

An autonomous, environmentally synchronizable circadian rhythm is a ubiquitous feature of life on Earth. In multicellular organisms, this rhythm is generated by a transcription-translation feedback loop present in nearly every cell that drives daily expression of thousands of genes in a tissue-dependent manner. Identifying the genes that are under circadian control can elucidate the mechanisms by which physiological processes are coordinated in multicellular organisms. Today, transcriptomic profiling at the single-cell level provides an unprecedented opportunity to understand the function of cell-level clocks. However, while many cycling detection algorithms have been developed to identify genes under circadian control in bulk transcriptomic data, it is not known how best to adapt these algorithms to single-cell RNA seq data. Here, we benchmark commonly used circadian detection methods on their reliability and efficiency when applied to single-cell RNA seq data. Our results provide guidance on adapting existing cycling detection methods to the single-cell domain and elucidate opportunities for more robust and efficient rhythm detection in single-cell data. We also propose a subsampling procedure combined with harmonic regression as an efficient strategy to detect circadian genes in the single-cell setting.

Variational inference of single cell time series

Time course single-cell RNA sequencing (scRNA-seq) enables researchers to probe genome-wide expression dynamics at the the single cell scale. However, when gene expression is affected jointly by time and cellular identity, analyzing such data - including conducting cell type annotation and modeling cell type-dependent dynamics - becomes challenging. To address this problem, we propose SNOW (SiNgle cell flOW map), a deep learning algorithm to deconvolve single cell time series data into time-dependent and time-independent contributions. SNOW has a number of advantages. First, it enables cell type annotation based on the time-independent dimensions. Second, it yields a probabilistic model that can be used to discriminate between biological temporal variation and batch effects contaminating individual timepoints, and provides an approach to mitigate batch effects. Finally, it is capable of projecting cells forward and backward in time, yielding time series at the individual cell level. This enables gene expression dynamics to be studied without the need for clustering or pseudobulking, which can be error prone and result in information loss. We describe our probabilistic framework in detail and demonstrate SNOW using data from three distinct time course scRNA-seq studies. Our results show that SNOW is able to construct biologically meaningful latent spaces, remove batch effects, and generate realistic time-series at the single-cell level. By way of example, we illustrate how the latter may be used to enhance the detection of cell type-specific circadian gene expression rhythms, and may be readily extended to other time-series analyses.

Circadian rhythms of macrophages are altered by the acidic pH of the tumor microenvironment

Macrophages are prime therapeutic targets due to their pro-tumorigenic and immunosuppressive functions in tumors, but the varying efficacy of therapeutic approaches targeting macrophages highlights our incomplete understanding of how the tumor microenvironment (TME) can influence regulation of macrophages. The circadian clock is a key internal regulator of macrophage function, but how circadian rhythms of macrophages may be influenced by the tumor microenvironment remains unknown. We found that conditions associated with the TME such as polarizing stimuli, acidic pH, and elevated lactate concentrations can each alter circadian rhythms in macrophages. Circadian rhythms were enhanced in pro-resolution macrophages but suppressed in pro-inflammatory macrophages, and acidic pH had divergent effects on circadian rhythms depending on macrophage phenotype. While cyclic AMP (cAMP) has been reported to play a role in macrophage response to acidic pH, our results indicate that pH-driven changes in circadian rhythms are not mediated solely by the cAMP signaling pathway. Remarkably, clock correlation distance analysis of tumor-associated macrophages (TAMs) revealed evidence of circadian disorder in TAMs. This is the first report providing evidence that circadian rhythms of macrophages are altered within the TME. Our data further suggest that heterogeneity in circadian rhythms at the population level may underlie this circadian disorder. Finally, we sought to determine how circadian regulation of macrophages impacts tumorigenesis, and found that tumor growth was suppressed when macrophages had a functional circadian clock. Our work demonstrates a novel mechanism by which the tumor microenvironment can influence macrophage biology through altering circadian rhythms, and the contribution of circadian rhythms in macrophages to suppressing tumor growth.

tauFisher predicts circadian time from a single sample of bulk and single-cell pseudobulk transcriptomic data

As the circadian clock regulates fundamental biological processes, disrupted clocks are often observed in patients and diseased tissues. Determining the circadian time of the patient or the tissue of focus is essential in circadian medicine and research. Here we present tauFisher, a computational pipeline that accurately predicts circadian time from a single transcriptomic sample by finding correlations between rhythmic genes within the sample. We demonstrate tauFisher's performance in adding timestamps to both bulk and single-cell transcriptomic samples collected from multiple tissue types and experimental settings. Application of tauFisher at a cell-type level in a single-cell RNAseq dataset collected from mouse dermal skin implies that greater circadian phase heterogeneity may explain the dampened rhythm of collective core clock gene expression in dermal immune cells compared to dermal fibroblasts. Given its robustness and generalizability across assay platforms, experimental setups, and tissue types, as well as its potential application in single-cell RNAseq data analysis, tauFisher is a promising tool that facilitates circadian medicine and research.

Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

Across a range of biological processes, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. Single-cell RNA-sequencing (scRNA-seq) only measures temporal snapshots of gene expression. However, information on the underlying low-dimensional dynamics can be extracted using RNA velocity, which models unspliced and spliced RNA abundances to estimate the rate of change of gene expression. Available RNA velocity algorithms can be fragile and rely on heuristics that lack statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. Here, we develop a generative model of RNA velocity and a Bayesian inference approach that solves these problems. Our model couples velocity field and manifold estimation in a reformulated, unified framework, so as to coherently identify the parameters of an autonomous dynamical system. Focusing on the cell cycle, we implemented VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validated using live-imaging its ability to infer actual cell cycle periods. We benchmarked RNA velocity inference with sensitivity analyses and demonstrated one- and multiple-sample testing. We also conducted Markov chain Monte Carlo inference on the model, uncovering key relationships between gene-specific kinetics and our gene-independent velocity estimate. Finally, we applied VeloCycle to in vivo samples and in vitro genome-wide Perturb-seq, revealing regionally-defined proliferation modes in neural progenitors and the effect of gene knockdowns on cell cycle speed. Ultimately, VeloCycle expands the scRNA-seq analysis toolkit with a modular and statistically rigorous RNA velocity inference framework.

tauFisher accurately predicts circadian time from a single sample of bulk and single-cell transcriptomic data

As the circadian clock regulates fundamental biological processes, disrupted clocks are often observed in patients and diseased tissues. Determining the circadian time of the patient or the tissue of focus is essential in circadian medicine and research. Here we present tau-Fisher, a computational pipeline that accurately predicts circadian time from a single transcriptomic sample by finding correlations between rhythmic genes within the sample. We demonstrate tauFisher's out-standing performance in both bulk and single-cell transcriptomic data collected from multiple tissue types and experimental settings. Application of tauFisher at a cell-type level in a single-cell RNA-seq dataset collected from mouse dermal skin implies that greater circadian phase heterogeneity may explain the dampened rhythm of collective core clock gene expression in dermal immune cells compared to dermal fibroblasts. Given its robustness and generalizability across assay platforms, experimental setups, and tissue types, as well as its potential application in single-cell RNA-seq data analysis, tauFisher is a promising tool that facilitates circadian medicine and research.

PENN: Phase Estimation Neural Network on Gene Expression Data

With the continuous expansion of available transcriptomic data like gene expression, deep learning techniques are becoming more and more valuable in analyzing and interpreting them. The National Center for Biotechnology Information Gene Expression Omnibus (GEO) encompasses approximately 5 million gene expression datasets from animal and human subjects. Unfortunately, the majority of them do not have a recorded timestamps, hindering the exploration of the behavior and patterns of circadian genes. Therefore, predicting the phases of these unordered gene expression measurements can help understand the behavior of the circadian genes, thus providing valuable insights into the physiology, behaviors, and diseases of humans and animals. In this paper, we propose a novel approach to predict the phases of the un-timed samples based on a deep neural network architecture. It incorporates the potential periodic oscillation information of the cyclic genes into the objective function to regulate the phase estimation. To validate our method, we use mouse heart, mouse liver and temporal cortex of human brain dataset. Through our experiments, we demonstrate the effectiveness of our proposed method in predicting phases and uncovering rhythmic pattern in circadian genes.