The most important function of adipose tissue is its ability to store fat during periods of feeding, and release fats during periods of fasting and cold. This energy homeostatic activity of the adipose tissue is made possible by the synergistic metabolic functionality of distinct adipocyte-types residing in the tissue (tissue heterogeneity), as well as their developmental dynamics (tissue development). Although, these aspects of adipose tissue biology are very well understood in mice, there knowledge in humans remains poorly understood. Recently, the combination of next generation sequencing (NGS) and microfluidic platforms has led to a revolution in single-cell genomic studies, enabling measurement of molecular features in thousands of single cells at a time. In this body of work, I present novel assays, platforms, and dataset that enable new investigation into adipose tissue at the single cell level, and provide insight into the heterogeneity and developmental lineages within this important tissue type.

Although, advancements in NGS and microfluidic barcoding platforms have significantly increased the throughput of single-cell RNA-seq (scRNA-seq) measurements, many molecules that are critical to understanding the functional roles of cells in a complex tissue or organs, are not directly encoded in the genome, and therefore cannot be profiled with NGS. Lipids, for example, play a critical role in many metabolic processes, and are critical to characterizing an adipocyte's identity, but cannot be detected by sequencing. Recent developments in quantitative imaging, particularly coherent Raman scattering (CRS) techniques, have produced a suite of tools for studying lipid content in single cells. In Chapter 2, I review CRS imaging and computational image processing techniques for non-destructive profiling of dynamic changes in lipid composition and spatial distribution at the single-cell level.

In Chapter 3, I present a microfluidic platform called microfluidic cell barcoding and sequencing (uCB-seq) for combining scRNA-seq measurements with optical imaging measurements, thereby providing a comprehensive characterization of cellular identity at the single-cell level. uCB-seq is enabled by a novel fabrication method that preloads primers with known barcode sequences inside addressable reaction chambers of a microfluidic device. In addition to enabling multi-modal single-cell analysis, uCB-seq improves gene detection sensitivity, providing a scalable and accurate method for information-rich characterization of single cells.

In Chapter 4, I characterize transcript enrichment and detection bias in single-nuclei RNA-seq for mapping of distinct human adipocyte lineages. scRNA-seq enables molecular characterization of complex biological tissues at high resolution. The requirement of single-cell extraction, however, makes it challenging for profiling tissues such as adipose tissue where collection of intact single adipocytes is complicated by their fragile nature. For such tissues, single-nuclei extraction is often much more efficient and therefore single-nuclei RNA-sequencing (snRNA-seq) presents an alternative to scRNA-seq. However, nuclear transcripts represent only a fraction of the transcriptome in a single cell, with snRNA-seq marked with inherent transcript enrichment and detection biases. Therefore, snRNA-seq may be inadequate for mapping important transcriptional signatures in adipose tissue. In this study, I compare the transcriptomic landscape of single nuclei isolated from preadipocytes and mature adipocytes across human white and brown adipocyte lineages, with whole-cell transcriptome. I demonstrate that snRNA-seq is capable of identifying the broad cell types present in scRNA-seq at all states of adipogenesis. However, I also explore how and why the nuclear transcriptome is biased and limited, and how it can be advantageous. I robustly characterize the enrichment of nuclear-localized transcripts and adipogenic regulatory lncRNAs in snRNA-seq, while also providing a detailed understanding for the preferential detection of long genes upon using this technique. To remove such technical detection biases, I propose a normalization strategy for a more accurate comparison of nuclear and cellular data. Finally, I demonstrate successful integration of scRNA-seq and snRNA-seq datasets with existing bioinformatic tools. Overall, my results illustrate the applicability of snRNA-seq for characterization of cellular diversity in the adipose tissue.

Finally, in Chapter 5, I utilize snRNA-seq to generate the transcriptional landscape of human white and brown adipogenesis using an in vitro model system, derived from a single individual and a single anatomical location. In total, I generate snRNA-seq libraries from ~50,000 nuclei isolated from differentiating white and brown preadipocytes at 5 stages of adipogenesis. Using a custom bioinformatic strategy for cellular ordering across a continuum of maturation states, I reveal 5 distinct gene expression modules in both white and brown adipogenesis, each module highlighting the dynamics of biologically relevant functional processes. I identify potentially novel adipogenic as well thermogenic transcription factors, and investigate their involvement in Obesity by analyzing publicly available GWAS, RNA-seq and microarray datasets in lean vs obese humans. Overall, this study, for the first time, provides a comprehensive molecular understanding of both white and brown adipogenesis in humans, thereby serving as an an important resource and a reference to map the future in vivo adipogenic studies onto.