Deep Large-Scale Multitask Learning Network for Gene Expression Inference.

Gene expression profiling makes it possible to conduct many biological studies in a variety of fields due to its thorough characterization of cellular states under various experimental conditions. Despite recent advances in high-throughput technology, profiling an entire set of genomes is still difficult and expensive. Due to the high correlation between expression patterns of different genes, the aforementioned problem can be solved with a cost-effective approach that collects only a small subset of genes, called landmark genes, representing the entire set of genes, and infer the remaining genes, called target genes, using a computational model. There are several shallow and deep regression models in literature to estimate the expressions of target genes from the landmark genes. However, the shallow mostly have limited capacity in learning the nonlinear and complex gene expression data and are prone to underfitting, and the deep models generally do not take advantage of correlation among target genes in the learning process and suffer from overfitting. Considering the gene expression inference as a multitask learning problem, we propose a new deep multitask learning algorithm to tackle these issues. Our learning framework automatically learns the correlation between target genes and uses this knowledge to improve its generalization. Specifically, we utilize a subnetwork with low-dimensional latent variables to discover the relationships between target genes and enforce a seamless and easy to implement regularization to our deep regression model. Unlike the existing multitask learning methods that can only deal with dozens or hundreds of tasks, our algorithm is able to efficiently learn the relationships between ∼10,000 target genes and, thus, is scalable to a large number of tasks. Our proposed method outperforms the shallow and deep regression models for gene expression inference and alternative multitask learning algorithms on two large-scale datasets regardless of the network architecture.

Genome-Based Targeted Sequencing as a Reproducible Microbial Community Profiling Assay.

Current sequencing-based methods for profiling microbial communities rely on marker gene (e.g., 16S rRNA) or metagenome shotgun sequencing (mWGS) analysis. We present an approach based on a single-primer extension reaction using a highly multiplexed oligonucleotide probe pool. This approach, termed MA-GenTA (microbial abundances from genome tagged analysis), enables quantitative, straightforward, cost-effective microbiome profiling that combines desirable features of both 16S rRNA and mWGS strategies. The use of multiple probes per target genome and rigorous probe design criteria enabled robust determination of relative abundance. To test the utility of the MA-GenTA assay, probes were designed for 830 genome sequences representing bacteria present in mouse stool specimens. Comparison of the MA-GenTA data with mWGS data demonstrated excellent correlation down to 0.01% relative abundance and a similar number of organisms detected per sample. Despite the incompleteness of the reference database, nonmetric multidimensional scaling (NMDS) clustering based on the Bray-Curtis dissimilarity metric of sample groups was consistent between MA-GenTA, mWGS, and 16S rRNA data sets. MA-GenTA represents a potentially useful new method for microbiome community profiling based on reference genomes.IMPORTANCE New methods for profiling the microbial communities can create new approaches to understanding the composition and function of those communities. In this study, we combined bacterial genome-specific probe design with a highly multiplexed single primer extension reaction as a new method to profile microbial communities, using stool from various mouse strains as a test case. This method, termed MA-GenTA, was benchmarked against 16S rRNA gene sequencing and metagenome sequencing methods and delivered similar relative abundance and clustering data. Since the probes were generated from reference genomes, MA-GenTA was also able to provide functional pathway data for the stool microbiome in the assayed samples. The method is more informative than 16S rRNA analysis while being less costly than metagenome shotgun sequencing.

Health economic impact of a biopsy-based cell cycle gene expression assay in localized prostate cancer.

Background: Prior studies have established that broader incorporation of active surveillance, guided by additional prognostic tools, may mitigate the growing economic burden of localized prostate cancer in the USA. This study sought to further explore the potential of a particular gene expression-based prognostic tool to address this unmet need. Materials & methods: A deterministic, decision-analytic model was developed to estimate the economic impact of the Prolaris® test on a US commercial health plan. Results & conclusion: When adopted in patients classified by the American Urological Association as low or intermediate risk, the assay was projected to reduce costs by $1894 and $2129 per patient over 3 and 10 years, respectively, largely through the increased use of active surveillance.

Computational approaches to support comparative analysis of multiparametric tests: Modelling versus Training.

Multiparametric assays for risk stratification are widely used in the management of breast cancer, with applications being developed for a number of other cancer settings. Recent data from multiple sources suggests that different tests may provide different risk estimates at the individual patient level. There is an increasing need for robust methods to support cost effective comparisons of test performance in multiple settings. The derivation of similar risk classifications using genes comprising the following multi-parametric tests Oncotype DX® (Genomic Health.), Prosigna™ (NanoString Technologies, Inc.), MammaPrint® (Agendia Inc.) was performed using different computational approaches. Results were compared to the actual test results. Two widely used approaches were applied, firstly computational "modelling" of test results using published algorithms and secondly a "training" approach which used reference results from the commercially supplied tests. We demonstrate the potential for errors to arise when using a "modelling" approach without reference to real world test results. Simultaneously we show that a "training" approach can provide a highly cost-effective solution to the development of real-world comparisons between different multigene signatures. Comparisons between existing multiparametric tests is challenging, and evidence on discordance between tests in risk stratification presents further dilemmas. We present an approach, modelled in breast cancer, which can provide health care providers and researchers with the potential to perform robust and meaningful comparisons between multigene tests in a cost-effective manner. We demonstrate that whilst viable estimates of gene signatures can be derived from modelling approaches, in our study using a training approach allowed a close approximation to true signature results.

Association of 21-Gene Assay (OncotypeDX) Testing and Receipt of Chemotherapy in the Medicare Breast Cancer Patient Population Following Initial Adoption.

BACKGROUND: Our objective was to investigate why early studies regarding adoption of the 21-gene recurrence score (RS) assay did not show an initial reduction in the number of patients with breast cancer receiving real-world chemotherapy. MATERIALS AND METHODS: We addressed 2 sources of confounding suspected in previous studies: (1) the early time frame during the initial adoption phase of the RS assay, and (2) suspected selective, increased administration to patients more likely to have been chemotherapy candidates. To address selective use during initial adoption, we used updated SEER-Medicare data from 2004 and 2011. To address individual selection bias, we examined whether RS test utilization was negatively associated with rates of local chemotherapy use assessed at the hospital referral region level using conventional ordinary least squares and instrumental variable approaches to adjust for selection bias. RESULTS: A total of 26,009 patients met inclusion criteria. Assay use was associated with a decrease in absolute percentage use of chemotherapy of 4.5% (95% confidence interval [CI], 3.2%-5.7%), which was even more pronounced in sensitivity analyses limited to later study years (2008-2011), with a decrease of 6.8% (95% CI, 5.3%-8.3%). Instrumental variable models yielded similar point estimates but were insufficiently powered to draw conclusions. CONCLUSION: Receipt of the 21-gene assay was associated with decreased utilization of chemotherapy by 2008.

Assessment of the Value of Tumor Variation Profiling Perceived by Patients With Cancer.

Importance: Use of tumor molecular profiling (MP) is entering routine clinical practice; however, little is known about how much and why patients value MP. Objective: To examine the perceived value of MP to patients with advanced cancer and factors associated with perceived value. Design, Setting, and Participants: A cross-sectional survey that included willingness-to-pay trade-off scenarios was administered to participants after consent and before MP. A total of 777 participants (94% response rate) were recruited from the Molecular Screening and Therapeutics Program. Eligible patients had advanced solid cancers of any histologic type, were receiving or had completed their last line of effective therapy, had an Eastern Cooperative Oncology Group Performance Status 0 to 3, and had sufficient accessible tissue for MP. The participants were recruited between October 24, 2017, and March 12, 2019, and data analysis was conducted from March 13 to April 14, 2019. Main Outcomes and Measures: Willingness to pay for MP was assessed via hypothetical trade-off scenarios varying in the actionable return rate (1%, 20%, or 40%) and cost (A$0, A$300 [US$210], A$1000 [US $700], A$3000 [US $2100], or A$10 000 [US $7000]). Ordinal regressions were used to explore factors associated with willingness to have and pay for MP. Results: Of 777 participants (405 women [52%]; mean [SD] age, 55.47 [14.26] years), 689 patients (89%) would have MP for as little as a 1% actionable return rate. Fifty-six patients (7%) would require at least a 20% return rate and 11 patients (1%) would require at least a 40% return rate. Fifteen patients (2%) consistently chose not to have the test; 6 participants (0.8%) had missing values on this item. Participants were willing to pay a median of A$1000 if the actionable return rate was 1% and A$3000 for an actionable return rate of 20% to 40%. Of 762 individuals who agreed to testing, 482 patients (64%) were consistently unwilling to pay A$10 000, regardless of the actionable return rate. Patients born in Australia or New Zealand were more likely to want MP (eg, participants born in South Asia had an ordered odds for the tipping point of 7.74 [95% CI, 1.67-36.05; P = .009] times higher than Australian- and/or New Zealand-born participants). Patients born in Australia or New Zealand were also more willing to pay A$1000 or A$3000 (eg, participants born in Western Europe had an ordered odds for the tipping point for paying A$1000 of 1.74 [95% CI, 1.01-3.00; P = .048] times higher than Australian- and/or New Zealand-born participants). People with a medical- or science-related occupation and with more negative attitudes toward uncertainty were more likely to pay A$10 000 (eg, A$10 000 tipping point-ordered odds of participants with a medical- or science-related occupation was 0.49 [95% CI, 0.7-0.87; P = .02] times that of participants without a medical- or science-related occupation). Conclusions and Relevance: This study found apparent high interest in but lower willingness to pay for MP among patients with advanced cancer. Ability to pay may limit access to MP. Ongoing societal debate is required to establish the value of MP and whether subsidization is needed to ensure equity of access.

Gene Expression Profiling Tests for Early-Stage Invasive Breast Cancer: A Health Technology Assessment.

BACKGROUND: Breast cancer is a disease in which cells in the breast grow out of control. They often form a tumour that may be seen on an x-ray or felt as a lump.Gene expression profiling (GEP) tests are intended to help predict the risk of metastasis (spread of the cancer to other parts of the body) and to identify people who will most likely benefit from chemotherapy. We conducted a health technology assessment of four GEP tests (EndoPredict, MammaPrint, Oncotype DX, and Prosigna) for people with early-stage invasive breast cancer, which included an evaluation of effectiveness, safety, cost effectiveness, the budget impact of publicly funding GEP tests, and patient preferences and values. METHODS: We performed a systematic literature search of the clinical evidence. We assessed the risk of bias of each included study using either the Cochrane Risk of Bias tool, Prediction model Risk Of Bias ASsessment Tool (PROBAST), or Risk of Bias Assessment tool for Non-randomized Studies (RoBANS), depending on the type of study and outcome of interest, and the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We also performed a literature survey of the quantitative evidence of preferences and values of patients and providers for GEP tests.We performed an economic evidence review to identify published studies assessing the cost-effectiveness of each of the four GEP tests compared with usual care or with one another for people with early-stage invasive breast cancer. We adapted a decision-analytic model to compare the costs and outcomes of care that includes a GEP test with usual care without a GEP test over a lifetime horizon. We also estimated the budget impact of publicly funding GEP tests to be conducted in Ontario, compared with funding tests conducted through the out-of-country program and compared with no funding of tests in any location.To contextualize the potential value of GEP tests, we spoke with people who have been diagnosed with early-stage invasive breast cancer. RESULTS: We included 68 studies in the clinical evidence review. Within the lymph-node-negative (LN-) population, GEP tests can prognosticate the risk of distant recurrence (GRADE: Moderate) and may predict chemotherapy benefit (GRADE: Low). The evidence for prognostic and predictive ability (ability to indicate the risk of an outcome and ability to predict who will benefit from chemotherapy, respectively) was lower for the lymph-node-positive (LN+) population (GRADE: Very Low to Low). GEP tests may also lead to changes in treatment (GRADE: Low) and generally may increase physician confidence in treatment recommendations (GRADE: Low).Our economic evidence review showed that GEP tests are generally cost-effective compared with usual care.Our primary economic evaluation showed that all GEP test strategies were more effective (led to more quality-adjusted life-years [QALYs]) than usual care and can be considered cost-effective below a willingness-to-pay of $20,000 per QALY gained. There was some uncertainty in our results. At a willingness-to-pay of $50,000 per QALY gained, the probability of each test being cost-effective compared to usual care was 63.0%, 89.2%, 89.2%, and 100% for EndoPredict, MammaPrint, Oncotype DX, and Prosigna, respectively.Sensitivity analyses showed our results were robust to variation in subgroups considered (i.e., LN+ and premenopausal), discount rates, age, and utilities. However, cost parameter assumptions did influence our results. Our scenario analysis comparing tests showed Oncotype DX was likely cost-effective compared with MammaPrint, and Prosigna was likely cost-effective compared with EndoPredict. When the GEP tests were compared with a clinical tool, the cost-effectiveness of the tests varied. Assuming a higher uptake of GEP tests, we estimated the budget impact to publicly fund GEP tests in Ontario would be between $1.29 million (Year 1) and $2.22 million (Year 5) compared to the current scenario of publicly funded GEP tests through the out-of-country program.Gene expression profiling tests are valued by patients and physicians for the additional information they provide for treatment decision-making. Patients are satisfied with what they learn from GEP tests and feel GEP tests can help reduce decisional uncertainty and anxiety. CONCLUSIONS: Gene expression profiling tests can likely prognosticate the risk of distant recurrence and some tests may also predict chemotherapy benefit. In people with breast cancer that is ER+, LN-, and human epidermal growth factor receptor 2 (HER2)-negative, GEP tests are likely cost-effective compared with no testing. The GEP tests are also likely cost-effective in LN+ and premenopausal people. Compared with funding GEP tests through the out-of-country program, publicly funding GEP tests in Ontario would cost an additional $1 million to $2 million annually, assuming a higher uptake of tests. GEP tests are valued by both patients and physicians for chemotherapy treatment decision-making.

Prospective observational study on the impact of the 21-gene assay on treatment decisions and resources optimization in breast cancer patients in Lombardy: The BONDX study.

PURPOSE: Adjuvant treatment decisions in early breast cancer (eBC) have traditionally been driven by risk stratification based on clinical and pathological risk factors. The 21-gene Oncotype DX® assay has been validated as a predictive test for benefit from adjuvant chemotherapy (CT), hence assessing its impact in clinical decisions is of high interest. The objective of this study was to estimate the rate of adjuvant treatment decision modification impacted by the Recurrence Score® result, and the consequent budget impact. METHODS: The study was a multicentre, prospective, real-life experience in Lombardy (Italy) including consecutive patients with T1-T3, N0-N1a, and ER+/HER2-eBC with clinical-pathologic "intermediate risk" of relapse. The change in treatment recommendations was assessed before and after availability of Recurrence Score result. A budget model evaluated the implications of 21-gene testing in the study population. RESULTS: The overall proportion of CT recommendations was reduced from 24.6% to 15.2% after 21-gene testing, with a major impact in patients initially considered for CT plus hormone therapy (CHT). In these patients, the total budget was reduced, leading to a net saving of -€81,017. The greater the physician propensity to prescribe CHT, the higher the potential savings for the health system from sparing CT in most tested patients. CONCLUSIONS: Our real-life experience suggests that all intermediate-risk ER+/HER2-eBC patients who are initially deemed candidates for CHT should be tested with the 21-gene test. The potential to spare CT in at least half of them offers relevant advantages for patients and national health services.

A bronchial-airway gene-expression classifier to improve the diagnosis of lung cancer: Clinical outcomes and cost-effectiveness analysis.

Bronchoscopy is the safest procedure for lung cancer diagnosis when an invasive evaluation is required after imaging procedures. However, its sensitivity is relatively low, especially for small and peripheral lesions. We assessed benefits and costs of introducing a bronchial gene-expression classifier (BGC) to improve the performance of bronchoscopy and the overall diagnostic process for early detection of lung cancer. We used discrete-event simulation to compare clinical and economic outcomes of two different strategies with the standard practice in former and current smokers with indeterminate nodules: (i) location-based strategy-integrated the BGC to the bronchoscopy indication; (ii) simplified strategy-extended use of bronchoscopy plus BGC also on small and peripheral lesions. Outcomes modeled were rate of invasive procedures, quality-adjusted-life-years (QALYs), costs and incremental cost-effectiveness ratios. Compared to the standard practice, the location-based strategy (i) reduced absolute rate of invasive procedures by 3.3% without increasing costs at the current BGC market price. It resulted in savings when the BGC price was less than $3,000. The simplified strategy (ii) reduced absolute rate of invasive procedures by 10% and improved quality-adjusted life expectancy, producing an incremental cost-effectiveness ratio of $10,109 per QALY. In patients with indeterminate nodules, both BGC strategies reduced unnecessary invasive procedures at high risk of adverse events. Moreover, compared to the standard practice, the simplified use of BGC for central and peripheral lesions resulted in larger QALYs gains at acceptable cost. The location-based is cost-saving if the price of classifier declines.

Selecting Patients for Oncotype DX Testing Using Standard Clinicopathologic Information.

INTRODUCTION: Indiscriminate ordering of Oncotype DX (ODX) is expensive and of poor value to patients, physicians, and health care providers. The 3 Magee equations, Gage Algorithm, and University of Tennessee predictive algorithm all use standard clinicopathologic data to provide surrogate ODX scores. In this hypothesis-generating study, we evaluated whether these prognostic scores could be used to identify patients unlikely to benefit from additional ODX testing. PATIENTS AND METHODS: Retrospective data was collected from 302 patients with invasive ductal breast cancer and available ODX scores. Additional data was available for: Magee equations 1 (212 patients), 2 (299 patients), 3 (212 patients), Gage Algorithm (299 patients), and University of Tennessee predictive algorithm (286 patients). ODX scores were banded according to the TAILORx results. RESULTS: Correlation with ODX scores was between 0.7 and 0.8 (Gage), 0.8 and 0.9 (Magee 2, University of Tennessee predictive algorithm), and > 0.9 (Magee 1 and 3). Magee 3 was the most robust and is proposed as a screening tool: for patients aged ≤ 50 years, ODX testing would be not required if the Magee 3 score was < 14 or ≥ 20; for those aged > 50 years, ODX would not be required if the Magee 3 score was < 18 or ≥ 26. Using these cut-offs, 110 (51.9%) of 212 patients would be deemed as not requiring ODX testing, and 109 (99.1%) of110 patients would be appropriately managed. CONCLUSIONS: Use of all formulae, and the Magee 3 equation in particular, are proposed as possible screening tools for ODX testing, resulting in significantly reduced frequency of ODX testing. This requires validation in other populations.

Cost effective, experimentally robust differential-expression analysis for human/mammalian, pathogen and dual-species transcriptomics.

As sequencing read length has increased, researchers have quickly adopted longer reads for their experiments. Here, we examine 14 pathogen or host-pathogen differential gene expression data sets to assess whether using longer reads is warranted. A variety of data sets was used to assess what genomic attributes might affect the outcome of differential gene expression analysis including: gene density, operons, gene length, number of introns/exons and intron length. No genome attribute was found to influence the data in principal components analysis, hierarchical clustering with bootstrap support, or regression analyses of pairwise comparisons that were undertaken on the same reads, looking at all combinations of paired and unpaired reads trimmed to 36, 54, 72 and 101 bp. Read pairing had the greatest effect when there was little variation in the samples from different conditions or in their replicates (e.g. little differential gene expression). But overall, 54 and 72 bp reads were typically most similar. Given differences in costs and mapping percentages, we recommend 54 bp reads for organisms with no or few introns and 72 bp reads for all others. In a third of the data sets, read pairing had absolutely no effect, despite paired reads having twice as much data. Therefore, single-end reads seem robust for differential-expression analyses, but in eukaryotes paired-end reads are likely desired to analyse splice variants and should be preferred for data sets that are acquired with the intent to be community resources that might be used in secondary data analyses.

Prospective, multicenter study on the economic and clinical impact of gene-expression assays in early-stage breast cancer from a single region: the PREGECAM registry experience.

INTRODUCTION: The aim of this study is to evaluate the cost-effectiveness and impact of gene-expression assays (GEAs) on treatment decisions in a real-world setting of early-stage breast cancer (ESBC) patients. METHODS: This is a regional, prospective study promoted by the Council Health Authorities in Madrid. Enrolment was offered to women with estrogen receptor-positive, human epidermal growth factor receptor 2-negative, node-negative or micrometastatic, stage I or II breast cancer from 21 hospitals in Madrid. Treatment recommendations were recorded before and after knowledge of tests results. An economic model compared the cost-effectiveness of treatment, guided by GEAs or by common prognostic factors. RESULTS: 907 tests (440 Oncotype DX® and 467 MammaPrint®) were performed between February 2012 and November 2014. Treatment recommendation changed in 42.6% of patients. The shift was predominantly from chemohormonal (CHT) to hormonal therapy (HT) alone, in 30.5% of patients. GEAs increased patients' confidence in treatment decision making. Tumor grade, progesterone receptor positivity and Ki67 expression were associated with the likelihood of change from CHT to HT (P < 0.001) and from HT to CHT (P < 0.001). Compared with current clinical practice genomic testing increased quality-adjusted life years by 0.00787 per patient and was cost-saving from a national health care system (by 13.867€ per patient) and from a societal perspective (by 32.678€ per patient). CONCLUSION: Using GEAs to guide adjuvant therapy in ESBC is cost-effective in Spain and has a significant impact on treatment decisions.

Cost-effectiveness analysis of multigene expression profiling assays to guide adjuvant therapy decisions in women with invasive early-stage breast cancer.

Gene expression profiling (GEP) testing using 12-gene recurrence score (RS) assay (EndoPredict®), 58-gene RS assay (Prosigna®), and 21-gene RS assay (Oncotype DX®) is available to aid in chemotherapy decision-making when traditional clinicopathological predictors are insufficient to accurately determine recurrence risk in women with axillary lymph node-negative, hormone receptor-positive, and human epidermal growth factor-receptor 2-negative early-stage breast cancer. We examined the cost-effectiveness of incorporating these assays into standard practice. A decision model was built to project lifetime clinical and economic consequences of different adjuvant treatment-guiding strategies. The model was parameterized using follow-up data from a secondary analysis of the Anastrozole or Tamoxifen Alone or Combined randomized trial, cost data (2017 Canadian dollars) from the London Regional Cancer Program (Canada) and secondary Canadian sources. The 12-gene, 58-gene, and 21-gene RS assays were associated with cost-effectiveness ratios of $36,274, $48,525, and $74,911/quality-adjusted life year (QALY) gained and resulted in total gains of 379, 284.3, and 189.5 QALYs/year and total budgets of $12.9, $14.2, and $16.6 million/year, respectively. The total expected-value of perfect information about GEP assays' utility was $10.4 million/year. GEP testing using any of these assays is likely clinically and economically attractive. The 12-gene and 58-gene RS assays may improve the cost-effectiveness of GEP testing and offer higher value for money, although prospective evidence is still needed. Comparative field evaluations of GEP assays in real-world practice are associated with a large societal benefit and warranted to determine the optimal and most cost-effective assay for routine use.

Scalable and cost-effective ribonuclease-based rRNA depletion for transcriptomics.

Bacterial RNA sequencing (RNA-seq) is a powerful approach for quantitatively delineating the global transcriptional profiles of microbes in order to gain deeper understanding of their physiology and function. Cost-effective bacterial RNA-seq requires efficient physical removal of ribosomal RNA (rRNA), which otherwise dominates transcriptomic reads. However, current methods to effectively deplete rRNA of diverse non-model bacterial species are lacking. Here, we describe a probe and ribonuclease based strategy for bacterial rRNA removal. We implemented the method using either chemically synthesized oligonucleotides or amplicon-based single-stranded DNA probes and validated the technique on three novel gut microbiota isolates from three distinct phyla. We further showed that different probe sets can be used on closely related species. We provide a detailed methods protocol, probe sets for >5000 common microbes from RefSeq, and an online tool to generate custom probe libraries. This approach lays the groundwork for large-scale and cost-effective bacterial transcriptomics studies.

An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome.

Simultaneous profiling of transcriptome and chromatin accessibility within single cells is a powerful approach to dissect gene regulatory programs in complex tissues. However, current tools are limited by modest throughput. We now describe an ultra high-throughput method, Paired-seq, for parallel analysis of transcriptome and accessible chromatin in millions of single cells. We demonstrate the utility of Paired-seq for analyzing the dynamic and cell-type-specific gene regulatory programs in complex tissues by applying it to mouse adult cerebral cortex and fetal forebrain. The joint profiles of a large number of single cells allowed us to deconvolute the transcriptome and open chromatin landscapes in the major cell types within these brain tissues, infer putative target genes of candidate enhancers, and reconstruct the trajectory of cellular lineages within the developing forebrain.

BART-Seq: cost-effective massively parallelized targeted sequencing for genomics, transcriptomics, and single-cell analysis.

We describe a highly sensitive, quantitative, and inexpensive technique for targeted sequencing of transcript cohorts or genomic regions from thousands of bulk samples or single cells in parallel. Multiplexing is based on a simple method that produces extensive matrices of diverse DNA barcodes attached to invariant primer sets, which are all pre-selected and optimized in silico. By applying the matrices in a novel workflow named Barcode Assembly foR Targeted Sequencing (BART-Seq), we analyze developmental states of thousands of single human pluripotent stem cells, either in different maintenance media or upon Wnt/ß-catenin pathway activation, which identifies the mechanisms of differentiation induction. Moreover, we apply BART-Seq to the genetic screening of breast cancer patients and identify BRCA mutations with very high precision. The processing of thousands of samples and dynamic range measurements that outperform global transcriptomics techniques makes BART-Seq first targeted sequencing technique suitable for numerous research applications.

CEL-Seq2-Single-Cell RNA Sequencing by Multiplexed Linear Amplification.

Single-cell RNA sequencing has revolutionized the way we look at cell populations. Of the methods available, CEL-Seq was the first to use linear RNA amplification. With early barcoding and 3' sequencing, it is sensitive, cost-effective and easy to perform. Here we describe a protocol for performing CEL-Seq2 on sorted cells, which can be performed without any special equipment.

Single-Cell RNA Sequencing with Drop-Seq.

Drop-Seq is a low-cost, high-throughput platform to profile thousands of cells by encapsualting them into individual droplets. Uniquely barcoded mRNA capture microparticles and cells are coconfined through a microfluidic device within the droplets where they undergo cell lysis and RNA hybridiztion. After breaking the droplets and pooling the hybridized particles, reverse transcription, PCR, and sequencing in single reactions allow to generate data from thousands of single-cell transcriptomes while maintaining information on the cellular origin of each transcript.

Seq-Well: A Sample-Efficient, Portable Picowell Platform for Massively Parallel Single-Cell RNA Sequencing.

Seq-Well is a low-cost picowell platform that can be used to simultaneously profile the transcriptomes of thousands of cells from diverse, low input clinical samples. In Seq-Well, uniquely barcoded mRNA capture beads and cells are co-confined in picowells that are sealed using a semipermeable membrane, enabling efficient cell lysis and mRNA capture. The beads are subsequently removed and processed in parallel for sequencing, with each transcript's cell of origin determined via the unique barcodes. Due to its simplicity and portability, Seq-Well can be performed almost anywhere.