Cell Type-Specific Survey of Epigenetic Modifications by Tandem Chromatin Immunoprecipitation Sequencing

Molecular profiling of single neurons of known identity in two ganglia from the crab Cancer borealis

Single-cell transcriptional profiling has become a widespread tool in cell identification, particularly in the nervous system, based on the notion that genomic information determines cell identity. However, many cell-type classification studies are unconstrained by other cellular attributes (e.g., morphology, physiology). Here, we systematically test how accurately transcriptional profiling can assign cell identity to well-studied anatomically and functionally identified neurons in 2 small neuronal networks. While these neurons clearly possess distinct patterns of gene expression across cell types, their expression profiles are not sufficient to unambiguously confirm their identity. We suggest that true cell identity can only be determined by combining gene expression data with other cellular attributes such as innervation pattern, morphology, or physiology.

Understanding circuit organization depends on identification of cell types. Recent advances in transcriptional profiling methods have enabled classification of cell types by their gene expression. While exceptionally powerful and high throughput, the ground-truth validation of these methods is difficult: If cell type is unknown, how does one assess whether a given analysis accurately captures neuronal identity? To shed light on the capabilities and limitations of solely using transcriptional profiling for cell-type classification, we performed 2 forms of transcriptional profiling—RNA-seq and quantitative RT-PCR, in single, unambiguously identified neurons from 2 small crustacean neuronal networks: The stomatogastric and cardiac ganglia. We then combined our knowledge of cell type with unbiased clustering analyses and supervised machine learning to determine how accurately functionally defined neuron types can be classified by expression profile alone. The results demonstrate that expression profile is able to capture neuronal identity most accurately when combined with multimodal information that allows for post hoc grouping, so analysis can proceed from a supervised perspective. Solely unsupervised clustering can lead to misidentification and an inability to distinguish between 2 or more cell types. Therefore, this study supports the general utility of cell identification by transcriptional profiling, but adds a caution: It is difficult or impossible to know under what conditions transcriptional profiling alone is capable of assigning cell identity. Only by combining multiple modalities of information such as physiology, morphology, or innervation target can neuronal identity be unambiguously determined.

Oxygen Tension Strongly Influences Metabolic Parameters and the Release of Interleukin-6 of Human Amniotic Mesenchymal Stromal Cells In Vitro

The human amniotic membrane (hAM) has been used for tissue regeneration for over a century. In vivo (in utero), cells of the hAM are exposed to low oxygen tension (1-4% oxygen), while the hAM is usually cultured in atmospheric, meaning high, oxygen tension (20% oxygen). We tested the influence of oxygen tensions on mitochondrial and inflammatory parameters of human amniotic mesenchymal stromal cells (hAMSCs). Freshly isolated hAMSCs were incubated for 4 days at 5% and 20% oxygen. We found 20% oxygen to strongly increase mitochondrial oxidative phosphorylation, especially in placental amniotic cells. Oxygen tension did not impact levels of reactive oxygen species (ROS); however, placental amniotic cells showed lower levels of ROS, independent of oxygen tension. In contrast, the release of nitric oxide was independent of the amniotic region but dependent on oxygen tension. Furthermore, IL-6 was significantly increased at 20% oxygen. To conclude, short-time cultivation at 20% oxygen of freshly isolated hAMSCs induced significant changes in mitochondrial function and release of IL-6. Depending on the therapeutic purpose, cultivation conditions of the cells should be chosen carefully for providing the best possible quality of cell therapy.

A droplet microfluidic platform for efficient enzymatic chromatin digestion enables robust determination of nucleosome positioning

The first step in chromatin-based epigenetic assays involves the fragmentation of chromatin to facilitate precise genomic localization of the associated DNA. Here, we report the development of a droplet microfluidic device that can rapidly and efficiently digest chromatin into single nucleosomes starting from whole-cell input material offering simplified and automated processing compared to conventional manual preparation. We demonstrate the digestion of chromatin from 2500-125 000 Jurkat cells using micrococcal nuclease for enzymatic processing. We show that the yield of mononucleosomal DNA can be optimized by controlling enzyme concentration and incubation time, with resulting mononucleosome yields exceeding 80%. Bioinformatic analysis of sequenced mononucleosomal DNA (MNase-seq) indicated a high degree of reproducibility and concordance (97-99%) compared with conventionally processed preparations. Our results demonstrate the feasibility of robust and automated nucleosome preparation using a droplet microfluidic platform for nucleosome positioning and downstream epigenomic assays.