SignificanceReprogramming the human genome toward any desirable state is within reach; application of select transcription factors drives cell types toward different lineages in many settings. We introduce the concept of data-guided control in building a universal algorithm for directly reprogramming any human cell type into any other type. Our algorithm is based on time series genome transcription and architecture data and known regulatory activities of transcription factors, with natural dimension reduction using genome architectural features. Our algorithm predicts known reprogramming factors, top candidates for new settings, and ideal timing for application of transcription factors. This framework can be used to develop strategies for tissue regeneration, cancer cell reprogramming, and control of dynamical systems beyond cell biology.

Increased adipose tissue macrophages (ATM) correlate with metabolic dysfunction in humans and are causal in development of insulin resistance in mice. Recent bulk and single cell transcriptomics studies reveal a wide spectrum of gene expression signatures possible for macrophages that depends on context, but the signatures of human ATM subtypes are not well defined in obesity and diabetes. We profiled three prominent ATM subtypes from human adipose tissue in obesity and determined their relationship to type 2 diabetes. Visceral (VAT) and subcutaneous (SAT) adipose tissue samples were collected from diabetic and non-diabetic obese subjects to evaluate cellular content and gene expression. VAT CD206 + CD11c − ATMs were increased in diabetic subjects, scavenger receptor-rich with low intracellular lipids, secreted proinflammatory cytokines, and diverged significantly from two CD11c + ATM subtypes, which were lipid-laden, lipid antigen presenting, and overlapped with monocyte signatures. Furthermore, diabetic VAT was enriched for CD206 + CD11c − ATM and inflammatory signatures, scavenger receptors, and MHC II antigen presentation genes. VAT immunostaining found CD206 + CD11c⁻ ATMs concentrated in vascularized lymphoid clusters adjacent to CD206⁻CD11c + ATMs, while CD206 + CD11c + were distributed between adipocytes. Our results suggest ATM subtype-specific profiles that uniquely contribute to the phenotypic variation in obesity.

Data on genome organization and output over time, or the 4D Nucleome (4DN), require synthesis for meaningful interpretation. Development of tools for the efficient integration of these data is needed, especially for the time dimension. We present the ‘4DNvestigator’, a user-friendly network-based toolbox for the analysis of time series genome-wide genome structure (Hi-C) and gene expression (RNA-seq) data. Additionally, we provide methods to quantify network entropy, tensor entropy, and statistically significant changes in time series Hi-C data at different genomic scales.

The combined analysis of genome structure and function over time, and how these changes affect cellular phenotype, is referred to as the 4D Nucleome (4DN). 4DN analysis is necessary to fully understand how a cell operates, but 4DN analysis tools are currently underdeveloped. We present the "4DNvestigator", a user-friendly toolbox for the analysis of time-series genome structure, measured by genome-wide chromosome conformation capture (Hi-C), and genome function, measured by RNA sequencing (RNAseq).

The day we understand the time evolution of subcellular elements at a level of detail comparable to physical systems governed by Newton's laws of motion seems far away. Even so, quantitative approaches to cellular dynamics add to our understanding of cell biology, providing data-guided frameworks that allow us to develop better predictions about, and methods for, control over specific biological processes and system-wide cell behavior. In this paper, we describe an approach to optimizing the use of transcription factors (TFs) in the context of cellular reprogramming. We construct an approximate model for the natural evolution of a cell cycle synchronized population of human fibroblasts, based on data obtained by sampling the expression of 22,083 genes at several time points along the cell cycle. In order to arrive at a model of moderate complexity, we cluster gene expression based on the division of the genome into topologically associating domains (TADs) and then model the dynamics of the TAD expression levels. Based on this dynamical model and known bioinformatics, such as transcription factor binding sites (TFBS) and functions, we develop a methodology for identifying the top transcription factor candidates for a specific cellular reprogramming task. The approach used is based on a device commonly used in optimal control. Our data-guided methodology identifies a number of transcription factors previously validated for reprogramming and/or natural differentiation. Our findings highlight the immense potential of dynamical models, mathematics, and data-guided methodologies for improving strategies for control over biological processes. Significance StatementReprogramming the human genome toward any desirable state is within reach; application of select transcription factors drives cell types toward different lineages in many settings. We introduce the concept of data-guided control in building a universal algorithm for directly reprogramming any human cell type into any other type. Our algorithm is based on time series genome transcription and architecture data and known regulatory activities of transcription factors, with natural dimension reduction using genome architectural features. Our algorithm predicts known reprogramming factors, top candidates for new settings, and ideal timing for application of transcription factors. This framework can be used to develop strategies for tissue regeneration, cancer cell reprogramming, and control of dynamical systems beyond cell biology.