The ability to study the molecular biology of living single cells in heterogeneous cell populations is essential for next generation analysis of cellular circuitry and function. Here, we developed a single-cell nanobiopsy platform based on scanning ion conductance microscopy (SICM) for continuous sampling of intracellular content from individual cells. The nanobiopsy platform uses electrowetting within a nanopipette to extract cellular material from living cells with minimal disruption of the cellular milieu. We demonstrate the subcellular resolution of the nanobiopsy platform by isolating small subpopulations of mitochondria from single living cells, and quantify mutant mitochondrial genomes in those single cells with high throughput sequencing technology. These findings may provide the foundation for dynamic subcellular genomic analysis.

The acute cellular response to stress generates a subpopulation of reversibly stress-tolerant cells under conditions that are lethal to the majority of the population. Stress tolerance is attributed to heterogeneity of gene expression within the population to ensure survival of a minority. We performed whole transcriptome sequencing analyses of metastatic human breast cancer cells subjected to the chemotherapeutic agent paclitaxel at the single-cell and population levels. Here we show that specific transcriptional programs are enacted within untreated, stressed, and drugtolerant cell groups while generating high heterogeneity between single cells within and between groups. We further demonstrate that drug-tolerant cells contain specific RNA variants residing in genes involved in microtubule organization and stabilization, as well as cell adhesion and cell surface signaling. In addition, the gene expression profile of drug-tolerant cells is similar to that of untreated cells within a few doublings. Thus, single-cell analyses reveal the dynamics of the stress response in terms of cell-specific RNA variants driving heterogeneity, the survival of a minority population through generation of specific RNA variants, and the efficient reconversion of stress-tolerant cells back to normalcy.

Correctly matching the HLA haplotypes of donor and recipient is essential to the success of allogenic hematopoietic stem cell transplantation. Current HLA typing methods rely on targeted testing of recognized antigens or sequences. Despite advances in Next Generation Sequencing, general high throughput transcriptome sequencing is currently underutilized for HLA haplotyping due to the central difficulty in aligning sequences within this highly variable region. Here we present the method, HLAforest, that can accurately predict HLA haplotype by hierarchically weighting reads and using an iterative, greedy, top down pruning technique. HLAforest correctly predicts >99% of allele group level (2 digit) haplotypes and 93% of peptide-level (4 digit) haplotypes of the most diverse HLA genes in simulations with read lengths and error rates modeling currently available sequencing technology. The method is very robust to sequencing error and can predict 99% of allele-group level haplotypes with substitution rates as high as 8.8%. When applied to data generated from a trio of cell lines, HLAforest corroborated PCR-based HLA haplotyping methods and accurately predicted 16/18 (89%) major class I genes for a daughter–father-mother trio at the peptide level. Major class II genes were predicted with 100% concordance between the daughter–father-mother trio. In fifty HapMap samples with paired end reads just 37 nucleotides long, HLAforest predicted 96.5% of allele group level HLA haplotypes correctly and 83% of peptide level haplotypes correctly. In sixteen RNAseq samples with limited coverage across HLA genes, HLAforest predicted 97.7% of allele group level haplotypes and 85% of peptide level haplotypes correctly.

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BackgroundThe nCounter analysis system (NanoString Technologies, Seattle, WA) is a technology that enables the digital quantification of multiplexed target RNA molecules using color-coded molecular barcodes and single-molecule imaging. This system gives discrete counts of RNA transcripts and is capable of providing a high level of precision and sensitivity at less than one transcript copy per cell.ResultsWe have designed a web application compatible with any modern web browser that accepts the raw count data produced by the NanoString nCounter analysis system, normalizes it according to guidelines provided by NanoString Technologies, performs differential expression analysis on the normalized data, and provides a heatmap of the results from the differential expression analysis.ConclusionNanoStriDE allows biologists to take raw data produced by a NanoString nCounter analysis system and easily interpret differential expression analysis of this data represented through a heatmap. NanoStriDE is freely accessible to use on the NanoStriDE website and is available to use under the GPL v2 license.