Characterization of transcript enrichment and detection bias in single-nucleus RNA-seq for mapping of distinct human adipocyte lineages.

Single-cell RNA sequencing (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, for which collection of intact single adipocytes is complicated by their fragile nature. For such tissues, single-nucleus extraction is often much more efficient and therefore single-nucleus 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, we compare the transcriptomic landscape of single nuclei isolated from preadipocytes and mature adipocytes across human white and brown adipocyte lineages, with whole-cell transcriptome. We show that snRNA-seq is capable of identifying the broad cell types present in scRNA-seq at all states of adipogenesis. However, we also explore how and why the nuclear transcriptome is biased and limited, as well as how it can be advantageous. We 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, we propose a normalization strategy for a more accurate comparison of nuclear and cellular data. Finally, we show successful integration of scRNA-seq and snRNA-seq data sets with existing bioinformatic tools. Overall, our results illustrate the applicability of snRNA-seq for the characterization of cellular diversity in the adipose tissue.

Quantitative imaging of lipid droplets in single cells.

The combination of next generation sequencing (NGS) and automated liquid handling platforms has led to a revolution in single-cell genomic studies. However, 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 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. This article reviews 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. As quantitative CRS imaging progresses synergistically with microfluidic and microscopic platforms for single-cell genomic analysis, we anticipate that these techniques will bring researchers closer towards combined lipidomic and genomic analysis.

PyLDM - An open source package for lifetime density analysis of time-resolved spectroscopic data.

Ultrafast spectroscopy offers temporal resolution for probing processes in the femto- and picosecond regimes. This has allowed for investigation of energy and charge transfer in numerous photoactive compounds and complexes. However, analysis of the resultant data can be complicated, particularly in more complex biological systems, such as photosystems. Historically, the dual approach of global analysis and target modelling has been used to elucidate kinetic descriptions of the system, and the identity of transient species respectively. With regards to the former, the technique of lifetime density analysis (LDA) offers an appealing alternative. While global analysis approximates the data to the sum of a small number of exponential decays, typically on the order of 2-4, LDA uses a semi-continuous distribution of 100 lifetimes. This allows for the elucidation of lifetime distributions, which may be expected from investigation of complex systems with many chromophores, as opposed to averages. Furthermore, the inherent assumption of linear combinations of decays in global analysis means the technique is unable to describe dynamic motion, a process which is resolvable with LDA. The technique was introduced to the field of photosynthesis over a decade ago by the Holzwarth group. The analysis has been demonstrated to be an important tool to evaluate complex dynamics such as photosynthetic energy transfer, and complements traditional global and target analysis techniques. Although theory has been well described, no open source code has so far been available to perform lifetime density analysis. Therefore, we introduce a python (2.7) based package, PyLDM, to address this need. We furthermore provide a direct comparison of the capabilities of LDA with those of the more familiar global analysis, as well as providing a number of statistical techniques for dealing with the regularization of noisy data.

Advances in engineering near-infrared luminescent materials.

Near-infrared (NIR) luminescent materials have emerged as a growing field of interest, particularly for imaging and optics applications in biology, chemistry, and physics. However, the development of materials for this and other use cases has been hindered by a range of issues that prevents their widespread use beyond benchtop research. This review explores emerging trends in some of the most promising NIR materials and their applications. In particular, we focus on how a more comprehensive understanding of intrinsic NIR material properties might allow researchers to better leverage these traits for innovative and robust applications in biological and physical sciences.

Advances in Nanomaterials for Brain Microscopy.

Microscopic imaging of the brain continues to reveal details of its structure, connectivity, and function. To further improve our understanding of the emergent properties and functions of neural circuits, new methods are necessary to directly visualize the relationship between brain structure, neuron activity, and neurochemistry. Advances in engineering the chemical and optical properties of nanomaterials concurrent with developments in deep-tissue microscopy hold tremendous promise for overcoming the current challenges associated with in vivo brain imaging, particularly for imaging the brain through optically-dense brain tissue, skull, and scalp. To this end, developments in nanomaterials offer much promise toward implementing tunable chemical functionality for neurochemical targeting and sensing, and fluorescence stability for long-term imaging. In this review, we summarize current brain microscopy methods and describe the diverse classes of nanomaterials recently leveraged as contrast agents and functional probes for microscopic optical imaging of the brain.