Neeraj Salathia

Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain

Single-nucleus gene expression Identifying the genes expressed at the level of a single cell nucleus can better help us understand the human brain. Blue et al. developed a single-nuclei sequencing technique, which they applied to cells in classically defined Brodmann areas from a postmortem brain. Clustering of gene expression showed concordance with the area of origin and defining 16 neuronal subtypes. Both excitatory and inhibitory neuronal subtypes show regional variations that define distinct cortical areas and exhibit how gene expression clusters may distinguish between distinct cortical areas. This method opens the door to widespread sampling of the genes expressed in a diseased brain and other tissues of interest. Science, this issue p. 1586 Individual neurons have specific transcriptomic signatures and transcriptomic heterogeneity. The human brain has enormously complex cellular diversity and connectivities fundamental to our neural functions, yet difficulties in interrogating individual neurons has impeded understanding of the underlying transcriptional landscape. We developed a scalable approach to sequence and quantify RNA molecules in isolated neuronal nuclei from a postmortem brain, generating 3227 sets of single-neuron data from six distinct regions of the cerebral cortex. Using an iterative clustering and classification approach, we identified 16 neuronal subtypes that were further annotated on the basis of known markers and cortical cytoarchitecture. These data demonstrate a robust and scalable method for identifying and categorizing single nuclear transcriptomes, revealing shared genes sufficient to distinguish previously unknown and orthologous neuronal subtypes as well as regional identity and transcriptomic heterogeneity within the human brain.

Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis

The transcriptional state of a cell reflects a variety of biological factors, from cell-type-specific features to transient processes such as the cell cycle, all of which may be of interest. However, identifying such aspects from noisy single-cell RNA-seq data remains challenging. We developed pathway and gene set overdispersion analysis (PAGODA) to resolve multiple, potentially overlapping aspects of transcriptional heterogeneity by testing gene sets for coordinated variability among measured cells.

Assessing characteristics of RNA amplification methods for single cell RNA sequencing

BackgroundRecently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known.ResultsHere, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate measurements to be quantitative at an expression level greater than ~5–10 molecules.ConclusionsBased on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.

Genetic suppression reveals DNA repair-independent antagonism between BRCA1 and COBRA1 in mammary gland development.

The breast cancer susceptibility gene BRCA1 is well known for its function in double-strand break (DSB) DNA repair. While BRCA1 is also implicated in transcriptional regulation, the physiological significance remains unclear. COBRA1 (also known as NELF-B) is a BRCA1-binding protein that regulates RNA polymerase II (RNAPII) pausing and transcription elongation. Here we interrogate functional interaction between BRCA1 and COBRA1 during mouse mammary gland development. Tissue-specific deletion of Cobra1 reduces mammary epithelial compartments and blocks ductal morphogenesis, alveologenesis and lactogenesis, demonstrating a pivotal role of COBRA1 in adult tissue development. Remarkably, these developmental deficiencies due to Cobra1 knockout are largely rescued by additional loss of full-length Brca1. Furthermore, Brca1/Cobra1 double knockout restores developmental transcription at puberty, alters luminal epithelial homoeostasis, yet remains deficient in homologous recombination-based DSB repair. Thus our genetic suppression analysis uncovers a previously unappreciated, DNA repair-independent function of BRCA1 in antagonizing COBRA1-dependent transcription programme during mammary gland development.

Development and Validation of an Ultradeep Next-Generation Sequencing Assay for Testing of Plasma Cell-Free DNA from Patients with Advanced Cancer

Purpose: Tumor-derived cell-free DNA (cfDNA) in plasma can be used for molecular testing and provide an attractive alternative to tumor tissue. Commonly used PCR-based technologies can test for limited number of alterations at the time. Therefore, novel ultrasensitive technologies capable of testing for a broad spectrum of molecular alterations are needed to further personalized cancer therapy. Experimental Design: We developed a highly sensitive ultradeep next-generation sequencing (NGS) assay using reagents from TruSeqNano library preparation and NexteraRapid Capture target enrichment kits to generate plasma cfDNA sequencing libraries for mutational analysis in 61 cancer-related genes using common bioinformatics tools. The results were retrospectively compared with molecular testing of archival primary or metastatic tumor tissue obtained at different points of clinical care. Results: In a study of 55 patients with advanced cancer, the ultradeep NGS assay detected 82% (complete detection) to 87% (complete and partial detection) of the aberrations identified in discordantly collected corresponding archival tumor tissue. Patients with a low variant allele frequency (VAF) of mutant cfDNA survived longer than those with a high VAF did (P = 0.018). In patients undergoing systemic therapy, radiological response was positively associated with changes in cfDNA VAF (P = 0.02), and compared with unchanged/increased mutant cfDNA VAF, decreased cfDNA VAF was associated with longer time to treatment failure (TTF; P = 0.03). Conclusions: Ultradeep NGS assay has good sensitivity compared with conventional clinical mutation testing of archival specimens. A high VAF in mutant cfDNA corresponded with shorter survival. Changes in VAF of mutated cfDNA were associated with TTF. Clin Cancer Res; 23(18); 5648–56. ©2017 AACR.

Feasibility of monitoring advanced melanoma patients using cell-free DNA from plasma

To determine the feasibility of liquid biopsy for monitoring of patients with advanced melanoma, cell‐free DNA was extracted from plasma for 25 Stage III/IV patients, most (84.0%) having received previous therapy. DNA concentrations ranged from 0.6 to 390.0 ng/ml (median = 7.8 ng/ml) and were positively correlated with tumor burden as measured by imaging (Spearman rho = 0.5435, p = .0363). Using ultra‐deep sequencing for a 61‐gene panel, one or more mutations were detected in 12 of 25 samples (48.0%), and this proportion did not vary significantly for patients on or off therapy at the time of blood draw (52.9% and 37.5% respectively; p = .673). Sixteen mutations were detected in eight different genes, with the most frequent mutations detected in BRAF, NRAS, and KIT. Allele fractions ranged from 1.1% to 63.2% (median = 29.1%). Among patients with tissue next‐generation sequencing, nine of 11 plasma mutations were also detected in matched tissue, for a concordance of 81.8%.

Assessing the measurement transfer function of single-cell RNA sequencing

Recently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known. Here, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate the measurement transfer functions to be linear above ~5-10 molecules. Based on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.

Abstract 2414: Novel, ultra deep next-generation sequencing of plasma cell-free DNA from patients with advanced cancers

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA Background: Plasma cell-free (cf) DNA from cancer patients offers an easily obtainable and repeatedly applicable source of DNA for mutation analysis, which provides attractive alternative to tumor tissue testing. Novel ultrasensitive multiplex technologies using small amounts of DNA are needed for further implementation of plasma cfDNA testing in personalized therapy. Methods: We have developed an ultra-deep next-generation sequencing method for somatic BRAF mutation detection in 5 ng of cfDNA with high sensitivity and specificity, which was expanded to include 30 (full exon tiling: ERBB2, TP53, FGFR1, PTEN, MET, KRAS, ALK, CTNNB1, KIT, NRAS, BRAF, PIK3CA, EGFR, MAP2K1, PIK3R1, DDR2, NF1, MITF, AKT3, PHLPP1, HGF, HOXD8, MEK2, hotspot containing exons: AKT1, HRAS, GNA11, GNAQ, RAC1; copy number variations: ERBB2, FGFR1, MET, BRAF, EGFR, MITF, HGF, HOXD8; gene fusions: RET1, ROS, and ALK) and then 58 common cancer related genes (previous panel + APC, AR, FGFR2, FGFR3, FGFR-4, SMO, FBXW7, NOTCH1, CDKN2B, IDH1, IDH2, GNAS, KDR, STK11, ESR1, VHL, ATM, CDH1, TRKA, TRKB, TRKC, TSC1, TSC2, Fltr3, FOXL2, CDKN2A, PDGFRA, IGF1R). Each cfDNA fragment was uniquely barcoded and amplified prior to Illumina target enrichment workflow, followed by ultra-deep sequencing (>10,000X). Proprietary data processing and analysis tools were developed to enable sensitive detection of rare mutant molecules over high wild-type background (detection of 1 in 1,000 - 10,000 molecules). Results were compared to mutation analysis of archival primary or metastatic tumor tissue obtained at different points of clinical care from a CLIA-certified laboratory. Results: Initially, cfDNA was extracted from plasma samples of 24 patients with advanced cancers (melanoma, n = 9; colorectal, n = 5; non-small cell lung, n = 2; papillary thyroid, n = 2; other cancers, n = 6) and 5ng were used for BRAF mutation analysis. BRAF mutations were detected in 71% (17/24) of plasma samples and in 88% (21/24) of archival tumor samples, resulting in concordance in 87% (20/24) of cases. Subsequently, we extracted plasma samples from additional 13 patients with advanced cancers (colorectal, n = 4; melanoma, n = 3; other cancers, n = 6) and tested 14-30ng of cfDNA for the presence of alterations in 30 to 58 cancer related genes. Both ultra-deep sequencing of plasma cfDNA and standard tissue sequencing detected median of 2 alterations per patient and dominant oncogenic alterations previously detected in the tumor tissue were also found in plasma cfDNA. Additional 80 samples are being analyzed and results will be presented at the meeting. Conclusions: Detecting common oncogenic alterations using ultra-deep sequencing of plasma cfDNA is feasible with an acceptable level of concordance with testing of tumor tissue and should be further investigated for testing in patients with advanced cancer. Citation Format: Filip Janku, Helen J. Huang, Nishma M. Ramzanali, David S. Hong, Daniel D. Karp, Xuyu Cai, Yue Zhao, Neeraj Salathia, Jill Waters, Li Liu, Rick Klausner, Funda Meric-Bernstam, Jian-Bing Fan. Novel, ultra deep next-generation sequencing of plasma cell-free DNA from patients with advanced cancers. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2414. doi:10.1158/1538-7445.AM2015-2414