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Measurement of gene expression at the single-cell level has led to important advances in the study of transcriptional regulation programs in healthy and disease states. In particular, single-cell gene expression approaches have shed light on the high level of transcriptional heterogeneity of individual cells, both at baseline and in response to experimental or environmental perturbations. We have developed a method for High-Content Imaging (HCI)-based quantification of transcript abundance at the single-cell level in primary human immune cells and have validated its performance under multiple experimental conditions to demonstrate its general applicability. This method, which we abbreviate as hcHCR, combines the high sensitivity of the hybridization chain reaction (HCR) for the visualization of mRNA molecules in single cells, with the speed, scalability, and technical reproducibility of HCI. We first tested eight microscopy-compatible attachment substrates for short-term culture of primary human B cells, T cells, monocytes, or neutrophils. We then miniaturized HCR in a 384-well format and documented the ability of the method to detect increased or decreased transcript abundance at the single-cell level in thousands of cells for each experimental condition by HCI. Furthermore, we demonstrated the feasibility of multiplexing gene expression measurements by simultaneously assaying the abundance of two transcripts per cell, both at baseline and in response to an experimental stimulus. Finally, we tested the robustness of the assay to technical and biological variation. We anticipate that hcHCR will be a suitable and cost-effective assay for low- to medium-throughput chemical, genetic or functional genomic screens in primary human cells, with the possibility of performing personalized screens or screens on cells obtained from patients with a specific disease.

Neutrophils are the most abundant leukocytes in human blood and are essential components of innate immunity. Until recently, neutrophils were considered homogeneous and transcriptionally inactive cells, but both concepts are being challenged. To date, neutrophils have been characterized based on discrete parameters including cell-surface markers, buoyancy, maturation status, or tissue localization. Single-cell RNA sequencing (scRNA-seq) offers an unbiased view of cells along a continuum of transcriptional states. However, the use of scRNA-seq to characterize neutrophils has proven technically difficult, explaining in part the paucity of published single-cell data on neutrophils. We have found that modifications to the data analysis pipeline, rather than to the existing scRNA-seq chemistries, can significantly increase the detection of human neutrophils in scRNA-seq. We have then applied a modified pipeline to the study of human peripheral blood neutrophils. Our findings indicate that circulating human neutrophils are transcriptionally heterogeneous cells, which can be classified into one of four transcriptional clusters that are reproducible among healthy human subjects. We demonstrate that peripheral blood neutrophils shift from relatively immature (Nh0) cells, through a transitional phenotype (Nh1), into one of two endpoints defined by either relative transcriptional inactivity (Nh2) or high expression of type I interferon-inducible genes (Nh3). Transitions among states are characterized by the expression of specific transcription factors. By simultaneously measuring surface proteins and intracellular transcripts at the single-cell level, we show that these transcriptional subsets are independent of the canonical surface proteins that are commonly used to define and characterize human neutrophils. These findings provide a new view of human neutrophil heterogeneity, with potential implications for the characterization of neutrophils in health and disease.

Differences between female and male immunity may contribute to variations in response to infections and predisposition to autoimmunity. We previously reported that neutrophils from reproductive-age males are more immature and less activated than their female counterparts. To further characterize the mechanisms that drive differential neutrophil phenotypes, we performed RNA sequencing on circulating neutrophils from healthy adult females and males. Female neutrophils displayed significant up-regulation of type I IFN (IFN)-stimulated genes (ISGs). Single-cell RNA-sequencing analysis indicated that these differences are neutrophil specific, driven by a distinct neutrophil subset and related to maturation status. Neutrophil hyperresponsiveness to type I IFNs promoted enhanced responses to Toll-like receptor agonists. Neutrophils from young adult males had significantly increased mitochondrial metabolism compared to those from females and this was modulated by estradiol. Assessment of ISGs and neutrophil maturation genes in Klinefelter syndrome (47, XXY) males and in prepubescent children supported that differences in neutrophil phenotype between adult male and female neutrophils are hormonally driven and not explained by X chromosome gene dosage. Our results indicate that there are distinct sex differences in neutrophil biology related to responses to type I IFNs, immunometabolism, and maturation status that may have prominent functional and pathogenic implications.

Deep learning is rapidly becoming the technique of choice for automated segmentation of nuclei in biological image analysis workflows. In order to evaluate the feasibility of training nuclear segmentation models on small, custom annotated image datasets that have been augmented, we have designed a computational pipeline to systematically compare different nuclear segmentation model architectures and model training strategies. Using this approach, we demonstrate that transfer learning and tuning of training parameters, such as the composition, size, and preprocessing of the training image dataset, can lead to robust nuclear segmentation models, which match, and often exceed, the performance of existing, off-the-shelf deep learning models pretrained on large image datasets. We envision a practical scenario where deep learning nuclear segmentation models trained in this way can be shared across a laboratory, facility, or institution, and continuously improved by training them on progressively larger and varied image datasets. Our work provides computational tools and a practical framework for deep learning-based biological image segmentation using small annotated image datasets. Published [2020]. This article is a U.S. Government work and is in the public domain in the USA.

Cancer has long been viewed as a genetic disease; however, epigenetic silencing as the result of aberrant promoter DNA methylation is frequently associated with cancer development, suggesting an epigenetic component to the disease. Nonetheless, it has remained unclear whether an epimutation (an aberrant change in epigenetic regulation) can induce tumorigenesis. Here, we exploited a functionally validated cis-acting regulatory element and devised a strategy to induce developmentally regulated genomic targeting of DNA methylation. We used this system to target DNA methylation within the p16(Ink4a) promoter in mice in vivo. Engineered p16(Ink4a) promoter hypermethylation led to transcriptional suppression in somatic tissues during aging and increased the incidence of spontaneous cancers in these mice. Further, mice carrying a germline p16(Ink4a) mutation in one allele and a somatic epimutation in the other had accelerated tumor onset and substantially shortened tumor-free survival. Taken together, these results provide direct functional evidence that p16(Ink4a) epimutation drives tumor formation and malignant progression and validate a targeted methylation approach to epigenetic engineering.

During development, a small but significant number of CpG islands (CGIs) become methylated. The timing of developmentally programmed CGI methylation and associated mechanisms of transcriptional regulation during cellular differentiation, however, remain poorly characterized. Here, we used genome-wide DNA methylation microarrays to identify epigenetic changes during human embryonic stem cell (hESC) differentiation. We discovered a group of CGIs associated with developmental genes that gain methylation after hESCs differentiate. Conversely, erasure of methylation was observed at the identified CGIs during subsequent reprogramming to induced pluripotent stem cells (iPSCs), further supporting a functional role for the CGI methylation. Both global gene expression profiling and quantitative reverse transcription-PCR (RT-PCR) validation indicated opposing effects of CGI methylation in transcriptional regulation during differentiation, with promoter CGI methylation repressing and 3' CGI methylation activating transcription. By studying diverse human tissues and mouse models, we further confirmed that developmentally programmed 3' CGI methylation confers tissue- and cell-type-specific gene activation in vivo. Importantly, luciferase reporter assays provided evidence that 3' CGI methylation regulates transcriptional activation via a CTCF-dependent enhancer-blocking mechanism. These findings expand the classic view of mammalian CGI methylation as a mechanism for transcriptional silencing and indicate a functional role for 3' CGI methylation in developmental gene regulation.

Supplemental Figures S1â S15. (PDF 2922 kb)

Supplemental Tables S1â S9. (PDF 2694 kb)

Supplemental Tables S1â S9. (PDF 2694 kb)