Large Gene Expression Datasets Research Articles

Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC

BackgroundUnderstanding the complex interactions between genes and their causal effects on diseases is crucial for developing targeted treatments and gaining insight into biological mechanisms. However, the analysis of molecular networks, especially in the context of high-dimensional data, presents significant challenges.MethodsThis study introduces MRdualPC, a computationally tractable algorithm based on the MRPC approach, to infer large-scale causal molecular networks. We apply MRdualPC to investigate the upstream causal transcriptomics influencing hypertension using a comprehensive dataset of kidney genome and transcriptome data.ResultsOur algorithm proves to be 100 times faster than MRPC on average in identifying transcriptomics drivers of hypertension. Through clustering, we identify 63 modules with causal driver genes, including 17 modules with extensive causal networks. Notably, we find that genes within one of the causal networks are associated with the electron transport chain and oxidative phosphorylation, previously linked to hypertension. Moreover, the identified causal ancestor genes show an over-representation of blood pressure-related genes.ConclusionsMRdualPC has the potential for broader applications beyond gene expression data, including multi-omics integration. While there are limitations, such as the need for clustering in large gene expression datasets, our study represents a significant advancement in building causal molecular networks, offering researchers a valuable tool for analyzing big data and investigating complex diseases.

Organ-delimited gene regulatory networks provide high accuracy in candidate transcription factor selection across diverse processes

Organ-specific gene expression datasets that include hundreds to thousands of experiments allow the reconstruction of organ-level gene regulatory networks (GRNs). However, creating such datasets is greatly hampered by the requirements of extensive and tedious manual curation. Here, we trained a supervised classification model that can accurately classify the organ-of-origin for a plant transcriptome. This K-Nearest Neighbor-based multiclass classifier was used to create organ-specific gene expression datasets for the leaf, root, shoot, flower, and seed in Arabidopsis thaliana. A GRN inference approach was used to determine the: i. influential transcription factors (TFs) in each organ and, ii. most influential TFs for specific biological processes in that organ. These genome-wide, organ-delimited GRNs (OD-GRNs), recalled many known regulators of organ development and processes operating in those organs. Importantly, many previously unknown TF regulators were uncovered as potential regulators of these processes. As a proof-of-concept, we focused on experimentally validating the predicted TF regulators of lipid biosynthesis in seeds, an important food and biofuel trait. Of the top 20 predicted TFs, eight are known regulators of seed oil content, e.g., WRI1, LEC1, FUS3. Importantly, we validated our prediction of MybS2, TGA4, SPL12, AGL18, and DiV2 as regulators of seed lipid biosynthesis. We elucidated the molecular mechanism of MybS2 and show that it induces purple acid phosphatase family genes and lipid synthesis genes to enhance seed lipid content. This general approach has the potential to be extended to any species with sufficiently large gene expression datasets to find unique regulators of any trait-of-interest.

Stable feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation for biomarker discovery in breast cancer

High-throughput technologies are becoming increasingly important in discovering prognostic biomarkers and in identifying novel drug targets. With Mammaprint, Oncotype DX, and many other prognostic molecular signatures breast cancer is one of the paradigmatic examples of the utility of high-throughput data to deliver prognostic biomarkers, that can be represented in a form of a rather short gene list. Such gene lists can be obtained as a set of features (genes) that are important for the decisions of a Machine Learning (ML) method applied to high-dimensional gene expression data. Several studies have identified predictive gene lists for patient prognosis in breast cancer, but these lists are unstable and have only a few genes in common. Instability of feature selection impedes biological interpretability: genes that are relevant for cancer pathology should be members of any predictive gene list obtained for the same clinical type of patients. Stability and interpretability of selected features can be improved by including information on molecular networks in ML methods. Graph Convolutional Neural Network (GCNN) is a contemporary deep learning approach applicable to gene expression data structured by a prior knowledge molecular network. Layer-wise Relevance Propagation (LRP) and SHapley Additive exPlanations (SHAP) are methods to explain individual decisions of deep learning models. We used both GCNN+LRP and GCNN+SHAP techniques to construct feature sets by aggregating individual explanations. We suggest a methodology to systematically and quantitatively analyze the stability, the impact on the classification performance, and the interpretability of the selected feature sets. We used this methodology to compare GCNN+LRP to GCNN+SHAP and to more classical ML-based feature selection approaches. Utilizing a large breast cancer gene expression dataset we show that, while feature selection with SHAP is useful in applications where selected features have to be impactful for classification performance, among all studied methods GCNN+LRP delivers the most stable (reproducible) and interpretable gene lists.

GS-TCGA: Gene Set-Based Analysis of The Cancer Genome Atlas.

Most tools for analyzing large gene expression datasets, including The Cancer Genome Atlas (TCGA), have focused on analyzing the expression of individual genes or inference of the abundance of specific cell types from whole transcriptome information. While these methods provide useful insights, they can overlook crucial process-based information that may enhance our understanding of cancer biology. In this study, we describe three novel tools incorporated into an online resource; gene set-based analysis of The Cancer Genome Atlas (GS-TCGA). GS-TCGA is designed to enable user-friendly exploration of TCGA data using gene set-based analysis, leveraging gene sets from the Molecular Signatures Database. GS-TCGA includes three unique tools: GS-Surv determines the association between the expression of gene sets and survival in human cancers. Co-correlative gene set enrichment analysis (CC-GSEA) utilizes interpatient heterogeneity in cancer gene expression to infer functions of specific genes based on GSEA of coregulated genes in TCGA. GS-Corr utilizes interpatient heterogeneity in cancer gene expression profiles to identify genes coregulated with the expression of specific gene sets in TCGA. Users are also able to upload custom gene sets for analysis with each tool. These tools empower researchers to perform survival analysis linked to gene set expression, explore the functional implications of gene coexpression, and identify potential gene regulatory mechanisms.

Genomic resources for a historical collection of cultivated two-row European spring barley genotypes

Barley genomic resources are increasing rapidly, with the publication of a barley pangenome as one of the latest developments. Two-row spring barley cultivars are intensely studied as they are the source of high-quality grain for malting and distilling. Here we provide data from a European two-row spring barley population containing 209 different genotypes registered for the UK market between 1830 to 2014. The dataset encompasses RNA-sequencing data from six different tissues across a range of barley developmental stages, phenotypic datasets from two consecutive years of field-grown trials in the United Kingdom, Germany and the USA; and whole genome shotgun sequencing from all cultivars, which was used to complement the RNA-sequencing data for variant calling. The outcomes are a filtered SNP marker file, a phenotypic database and a large gene expression dataset providing a comprehensive resource which allows for downstream analyses like genome wide association studies or expression associations.

A novel and innovative cancer classification framework through a consecutive utilization of hybrid feature selection

Cancer prediction in the early stage is a topic of major interest in medicine since it allows accurate and efficient actions for successful medical treatments of cancer. Mostly cancer datasets contain various gene expression levels as features with less samples, so firstly there is a need to eliminate similar features to permit faster convergence rate of classification algorithms. These features (genes) enable us to identify cancer disease, choose the best prescription to prevent cancer and discover deviations amid different techniques. To resolve this problem, we proposed a hybrid novel technique CSSMO-based gene selection for cancer classification. First, we made alteration of the fitness of spider monkey optimization (SMO) with cuckoo search algorithm (CSA) algorithm viz., CSSMO for feature selection, which helps to combine the benefit of both metaheuristic algorithms to discover a subset of genes which helps to predict a cancer disease in early stage. Further, to enhance the accuracy of the CSSMO algorithm, we choose a cleaning process, minimum redundancy maximum relevance (mRMR) to lessen the gene expression of cancer datasets. Next, these subsets of genes are classified using deep learning (DL) to identify different groups or classes related to a particular cancer disease. Eight different benchmark microarray gene expression datasets of cancer have been utilized to analyze the performance of the proposed approach with different evaluation matrix such as recall, precision, F1-score, and confusion matrix. The proposed gene selection method with DL achieves much better classification accuracy than other existing DL and machine learning classification models with all large gene expression dataset of cancer.

Characterizing the Extracellular Matrix Transcriptome of Endometriosis.

In recent years, the matrisome, a set of proteins that make up the extracellular matrix (ECM) or are closely involved in ECM behavior, has been shown to have great importance for characterizing and understanding disease pathogenesis and progression. The matrisome is especially critical for examining diseases characterized by extensive tissue remodeling. Endometriosis is characterized by the extrauterine growth of endometrial tissue, making it an ideal condition to study through the lens of matrisome gene expression. While large gene expression datasets have become more available and gene dysregulation in endometriosis has been the target of several studies, the gene expression profile of the matrisome specifically in endometriosis has not been well characterized. In our study, we explored four Gene Expression Omnibus (GEO) DNA microarray datasets containing eutopic endometrium of people with and without endometriosis. After batch correction, menstrual cycle phase accounted for 53% of variance and disease accounted for 23%; thus, the data were separated by menstrual cycle phase before performing differential expression analysis, statistical and machine learning modeling, and enrichment analysis. We established that matrisome gene expression alone can effectively differentiate endometriosis samples from healthy ones, demonstrating the potential of matrisome gene expression for diagnostic applications. Furthermore, we identified specific matrisome genes and gene networks whose expression can distinguish endometriosis stages I/II from III/IV. Taken together, these findings may aid in developing future in vitro models of disease, offer insights into novel treatment strategies, and advance diagnostic tools for this underserved patient population.

Enhancing Feature Selection Optimization for COVID-19 Microarray Data

The utilization of gene selection techniques is crucial when dealing with extensive datasets containing limited cases and numerous genes, as they enhance the learning processes and improve overall outcomes. In this research, we introduce a hybrid method that combines the binary reptile search algorithm (BRSA) with the LASSO regression method to effectively filter and reduce the dimensionality of a gene expression dataset. Our primary objective was to pinpoint genes associated with COVID-19 by examining the GSE149273 dataset, which focuses on respiratory viral (RV) infections in individuals with asthma. This dataset suggested a potential increase in ACE2 expression, a critical receptor for the SARS-CoV-2 virus, along with the activation of cytokine pathways linked to COVID-19. Our proposed BRSA method successfully identified six significant genes, including ACE2, IFIT5, and TRIM14, that are closely related to COVID-19, achieving an impressive maximum classification accuracy of 87.22%. By conducting a comparative analysis against four existing binary feature selection algorithms, we demonstrated the effectiveness of our hybrid approach in reducing the dimensionality of features, while maintaining a high classification accuracy. As a result, our hybrid approach shows great promise for identifying COVID-19-related genes and could be an invaluable tool for other studies dealing with very large gene expression datasets.

ARBic: an all-round biclustering algorithm for analyzing gene expression data.

Identifying significant biclusters of genes with specific expression patterns is an effective approach to reveal functionally correlated genes in gene expression data. However, none of existing algorithms can simultaneously identify both broader and narrower biclusters due to their failure of balancing between effectiveness and efficiency. We introduced ARBic, an algorithm which is capable of accurately identifying any significant biclusters of any shape, including broader, narrower and square, in any large scale gene expression dataset. ARBic was designed by integrating column-based and row-based strategies into a single biclustering procedure. The column-based strategy borrowed from RecBic, a recently published biclustering tool, extracts narrower biclusters, while the row-based strategy that iteratively finds the longest path in a specific directed graph, extracts broader ones. Being tested and compared to other seven salient biclustering algorithms on simulated datasets, ARBic achieves at least an average of 29% higher recovery, relevance and[Formula: see text] scores than the best existing tool. In addition, ARBic substantially outperforms all tools on real datasets and is more robust to noises, bicluster shapes and dataset types.

Simulating the restoration of normal gene expression from different thyroid cancer stages using deep learning

BackgroundThyroid cancer (THCA) is the most common endocrine malignancy and incidence is increasing. There is an urgent need to better understand the molecular differences between THCA tumors at different pathologic stages so appropriate diagnostic, prognostic, and treatment strategies can be applied. Transcriptome State Perturbation Generator (TSPG) is a tool created to identify the changes in gene expression necessary to transform the transcriptional state of a source sample to mimic that of a target.MethodsWe used TSPG to perturb the bulk RNA expression data from various THCA tumor samples at progressive stages towards the transcriptional pattern of normal thyroid tissue. The perturbations produced were analyzed to determine if there are consistently up- or down-regulated genes or functions in certain stages of tumors.ResultsSome genes of particular interest were investigated further in previous research. SLC6A15 was found to be down-regulated in all stage 1–3 samples. This gene has previously been identified as a tumor suppressor. The up-regulation of PLA2G12B in all samples was notable because the protein encoded by this gene belongs to the PLA2 superfamily, which is involved in metabolism, a major function of the thyroid gland. REN was up-regulated in all stage 3 and 4 samples. The enzyme renin encoded by this gene, has a role in the renin-angiotensin system; this system regulates angiogenesis and may have a role in cancer development and progression. This is supported by the consistent up-regulation of REN only in later stage tumor samples. Functional enrichment analysis showed that olfactory receptor activities and similar terms were enriched for the up-regulated genes which supports previous research concluding that abundance and stimulation of olfactory receptors is linked to cancer.ConclusionsTSPG can be a useful tool in exploring large gene expression datasets and extracting the meaningful differences between distinct classes of data. We identified genes that were characteristically perturbed in certain sample types, including only late-stage THCA tumors. Additionally, we provided evidence for potential transcriptional signatures of each stage of thyroid cancer. These are potentially relevant targets for future investigation into THCA tumorigenesis.

Frontiers of Ferroptosis in Cancer Treatment.

Recent phenomenal advancements in genomic and proteomic technologies and rapid breakthroughs in the interpretation of large gene expression datasets have enabled scientists to comprehensively characterize the gene signatures involved in ferroptosis. Ferroptosis is an iron-dependent form of non-apoptotic cell death that has gained the worthwhile attention of both basic and clinical researchers. Ferroptosis has dichotomous, context-dependent functions both as a tumor suppressor and promoter of carcinogenesis. Essentially, pharmacological modulation of ferroptosis by its induction as well as its inhibition holds enormous potential to overcome drug resistance and to improve the therapeutic potential of chemotherapeutic drugs in a wide variety of cancers.

High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0.

MotivationGene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above.ResultsIn this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator is able to integrate the largest single-cell datasets and learn cell-type-specific gene regulatory networks. Compared to other network inference methods, the Inferelator learns new and informative Saccharomyces cerevisiae networks from single-cell gene expression data, measured by recovery of a known gold standard. We demonstrate its scaling capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data.Availability and implementationThe inferelator software is available on GitHub (https://github.com/flatironinstitute/inferelator) under the MIT license and has been released as python packages with associated documentation (https://inferelator.readthedocs.io/).Supplementary information Supplementary data are available at Bioinformatics online.

A functional module states framework reveals transcriptional states for drug and target prediction.

Cells are complex systems in which many functions are performed by different genetically defined and encoded functional modules. To systematically understand how these modules respond to drug or genetic perturbations, we develop a functional module states framework. Using this framework, we (1) define the drug-induced transcriptional state space for breast cancer cell lines using large public gene expression datasets and reveal that the transcriptional states are associated with drug concentration and drug targets, (2) identify potential targetable vulnerabilities through integrative analysis of transcriptional states after drug treatment and gene knockdown-associated cancer dependency, and (3) use functional module states to predict transcriptional state-dependent drug sensitivity and build prediction models for drug response. This approach demonstrates a similar prediction performance as approaches using high-dimensional gene expression values, with the added advantage of more clearly revealing biologically relevant transcriptional states and key regulators.

Classification and Functional Analysis between Cancer and Normal Tissues Using Explainable Pathway Deep Learning through RNA-Sequencing Gene Expression.

Deep learning has proven advantageous in solving cancer diagnostic or classification problems. However, it cannot explain the rationale behind human decisions. Biological pathway databases provide well-studied relationships between genes and their pathways. As pathways comprise knowledge frameworks widely used by human researchers, representing gene-to-pathway relationships in deep learning structures may aid in their comprehension. Here, we propose a deep neural network (PathDeep), which implements gene-to-pathway relationships in its structure. We also provide an application framework measuring the contribution of pathways and genes in deep neural networks in a classification problem. We applied PathDeep to classify cancer and normal tissues based on the publicly available, large gene expression dataset. PathDeep showed higher accuracy than fully connected neural networks in distinguishing cancer from normal tissues (accuracy = 0.994) in 32 tissue samples. We identified 42 pathways related to 32 cancer tissues and 57 associated genes contributing highly to the biological functions of cancer. The most significant pathway was G-protein-coupled receptor signaling, and the most enriched function was the G1/S transition of the mitotic cell cycle, suggesting that these biological functions were the most common cancer characteristics in the 32 tissues.

High CD52 mRNA Expression Is An Adverse Prognostic Factor In Fludarabine-Refractory CLL Treated With Alemtuzumab On The Gcllsg CLL2H Trial

Alemtuzumab is a humanized mAb that targets CD52 and has been widely used for the treatment of chronic lymphocytic leukemia (CLL) patients with refractory disease or loss / mutation of TP53. The cell-surface marker CD52 is variably expressed on peripheral blood mononuclear cells and shows relatively high expression levels on the surface of both healthy B lymphocytes and on CLL cells, but the biologic function of CD52 has not been sufficiently clarified yet.To elucidate the prognostic value of CD52 expression, we analyzed a large gene expression dataset. Gene expression profiling was performed on Affymetrix Exon 1.0 ST Arrays. Data sets were generated for 75 fludarabine-refractory CLL patients treated within the multicenter phase II CLL2H trial of the GCLLSG (3x30 mg Alemtuzumab s.c. per week, max. 12 weeks). Normalization of the arrays was conducted by using the RMA algorithm. Statistical analysis was performed with the R software version 2.15.1.Patients were grouped into quartiles (Q1-Q4) according to CD52 expression levels (CD52 low: Q1, n=19; CD52 intermediate: Q2-Q3, n=37; CD52 high: Q4, n=19).Progression free survival (PFS) and overall survival (OS) were markedly different for patients from Q1 and Q4 (median PFS: Q1=413 days vs. Q4=145 days (p=0.0102); median OS: Q1= 879 days vs. Q4=413 days (p=0.0164)). Patients with progressive disease were overrepresented in Q4 (PD 7/18 (38.8%); ORR 5/18 (27.7%)) while rare in Q1 (PD 2/19 (10.5%); ORR 9/19 (47.3%)). Patients with low expression of CD52 had less frequently genetic/biologic and clinical characteristics of aggressive disease (Q1 vs. Q4; TP53 mutation/17p deletion: 47.36% vs. 36.84%; IGHV unmutated: 61.11% vs. 73.68%; leukocyte counts (median): 45.8 G/L vs. 55.4 G/L; thymidine kinase: 22.2 U/L vs. 30.4 U/L; β2-microglobulin: 3.3 mg/l vs. 5.2 mg/l; no palpable lymph nodes: 47.36 % vs. 10.52 %).Mutations of SF3B1 and NOTCH1 were present in 4/17 (23.5%) and in 5/17 (29.4%) cases in Q1. In contrast, cases from Q4 harbored mutations of SF3B1 and NOTCH1 in 2/18 (11.1%) and 1/18 (5.5%).Cases with high CD52 levels showed overexpression (FDR<1e-07;FC>2) of genes predominantly involved in signaling of the B-cell-, toll-like- and interleukin-receptor (BTK, SYK, CD79B; TLR7, TLR10, IL27RA, IL2RG, TRAF3IP3, TRAF5).The corresponding gene set overrepresentation analysis on KEGG pathways was significantly enriched for gene sets related to cell-extracellular matrix interaction, focal adhesion, regulation of actin cytoskeleton and cell adhesion molecules as well as associated signaling pathways like WNT- and MAPK-signaling.In conclusion, high CD52 expression is an adverse prognostic factor in refractory CLL treated with Alemtuzumab. Although suggestive, the level of CD52 expression and subsequent antibody binding capacity appear not to determine the effectivity of treatment. The association with genetic and biological characteristics indicates an independent role in the biology of aggressive CLL and highlights a potential function in cell-microenvironmental interactions for CLL cells. [Display omitted] Disclosures:Stilgenbauer:Genzyme: Honoraria, Research Funding.

Abstract 3179: Deconvolving bulk gene expression to chart the immune activity and crosstalk in the tumor microenvironment

Abstract The tumor microenvironment is a complex mixture of cell-types that interact with each other to affect tumor growth and clinical outcomes. To accelerate the discovery of such interactions, we develop CODEFACS (Confident Deconvolution For All Cell Subsets), a deconvolution tool that infers cell-type-specific gene expression in each sample from bulk expression measurements. We additionally developed LIRICS (Ligand Receptor Interactions between Cell Subsets), a pipeline that analyzes the deconvolved gene expression to further identify clinically relevant ligand-receptor interactions between cell-types. Using 15 benchmark datasets, we first demonstrate that CODEFACS substantially improves our ability to reconstruct cell-type-specific transcriptomes from individual bulk samples compared to the state-of-the-art method, CIBERSORTx. We next analyze the TCGA using CODEFACS and LIRICS to uncover tumor-immunological cell-type-specific interactions that are differentially activate in mismatch repair deficient tumors. This analysis shows that T-cell co-stimulating interactions are associated with higher survival rates in these tumors independent of their mutation burden levels. Finally, using machine learning, we identify key cell-type-specific ligand-receptor interactions that predict patient response to immune checkpoint blockade therapy in melanoma. Taken together, we present and validate a computational framework for analyzing large bulk gene expression datasets at a cell-type specific resolution that has many potential future applications, complementing single cell transcriptomics in a cost-effective manner. Citation Format: Kun Wang, Sushant Patkar, JooSang Lee, E. Michael Gertz, Welles Robinson, Fiorella Schischlik, Alejandro Schäffer, Eytan Ruppin. Deconvolving bulk gene expression to chart the immune activity and crosstalk in the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3179.

The importance of genomic predictors for clinical outcome of hematological malignancies

The importance of genomic predictors for clinical outcome of hematological malignancies

Generation and Characterization of a Cell Type-Specific, Inducible Cre-Driver Line to Study Olfactory Processing.

In sensory systems of the brain, mechanisms exist to extract distinct features from stimuli to generate a variety of behavioral repertoires. These often correspond to different cell types at various stages in sensory processing. In the mammalian olfactory system, complex information processing starts in the olfactory bulb, whose output is conveyed by mitral cells (MCs) and tufted cells (TCs). Despite many differences between them, and despite the crucial position they occupy in the information hierarchy, Cre-driver lines that distinguish them do not yet exist. Here, we sought to identify genes that are differentially expressed between MCs and TCs of the mouse, with an ultimate goal to generate a cell type-specific Cre-driver line, starting from a transcriptome analysis using a large and publicly available single-cell RNA-seq dataset (Zeisel et al., 2018). Many genes were differentially expressed, but only a few showed consistent expressions in MCs and at the specificity required. After further validating these putative markers using ISH, two genes (i.e., Pkib and Lbdh2) remained as promising candidates. Using CRISPR/Cas9-mediated gene editing, we generated Cre-driver lines and analyzed the resulting recombination patterns. This indicated that our new inducible Cre-driver line, Lbhd2-CreERT2, can be used to genetically label MCs in a tamoxifen dose-dependent manner, both in male and female mice, as assessed by soma locations, projection patterns, and sensory-evoked responses in vivo. Hence, this is a promising tool for investigating cell type-specific contributions to olfactory processing and demonstrates the power of publicly accessible data in accelerating science.SIGNIFICANCE STATEMENT In the brain, distinct cell types play unique roles. It is therefore important to have tools for studying unique cell types specifically. For the sense of smell in mammals, information is processed first by circuits of the olfactory bulb, where two types of cells, mitral cells and tufted cells, output different information. We generated a transgenic mouse line that enables mitral cells to be specifically labeled or manipulated. This was achieved by looking for genes that are specific to mitral cells using a large and public gene expression dataset, and creating a transgenic mouse using the gene editing technique, CRISPR/Cas9. This will allow scientists to better investigate parallel information processing underlying the sense of smell.

Iterative single-cell multi-omic integration using online learning.

Integrating large single-cell gene expression, chromatin accessibility and DNA methylation datasets requires general and scalable computational approaches. Here we describe online integrative nonnegative matrix factorization (iNMF), an algorithm for integrating large, diverse, and continually arriving single-cell datasets. Our approach scales to arbitrarily large numbers of cells using fixed memory, iteratively incorporates new datasets as they are generated, and allows many users to simultaneously analyze a single copy of a large dataset by streaming it over the internet. Iterative data addition can also be used to map new data to a reference dataset. Comparisons with previous methods indicate that the improvements in efficiency do not sacrifice dataset alignment and cluster preservation performance. We demonstrate the effectiveness of online iNMF by integrating more than a million cells on a standard laptop, integrating large single-cell RNA-seq and spatial transcriptomic datasets, and iteratively constructing a single-cell multi-omic atlas of the mouse motor cortex.

IGF2BP3 Associates with Proliferative Phenotype and Prognostic Features in B-Cell Acute Lymphoblastic Leukemia.

Simple SummaryAlthough the prognosis of acute lymphoblastic leukemia (ALL) has improved significantly during the past decades, ALL remains a major cause of pediatric cancer mortality, and more accurate risk-stratification is required. We investigated IGF2BP3, which has previously been associated with aggressive cancers, and found high and subtype-specific expression of IGF2BP3 in B-cell ALL, that was associated with good outcome in high-risk patients. Results suggest that IGF2BP3 could be useful to improve stratification and prognosis of B-ALL.The oncofetal protein insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3) belongs to a family of RNA-binding proteins involved in localization, stability, and translational regulation of target RNAs. IGF2BP3 is used as a diagnostic and prognostic marker in several malignancies. Although the prognosis of pediatric B-cell acute lymphoblastic leukemia (B-ALL) has improved, a subgroup of patients exhibits high-risk features and suffer from disease recurrence. We sought to identify additional biomarkers to improve diagnostics, and we assessed expression of IGF2BP3 in a population-based pediatric cohort of B-ALL using a tissue microarray platform. The majority of pediatric B-ALL cases were positive for IGF2BP3 immunohistochemistry and were associated with an increased proliferative phenotype and activated STAT5 signaling pathway. Two large gene expression data sets were probed for the expression of IGF2BP3—the highest levels were seen among the B-cell lymphomas of a germinal center origin and well-established (KMT2A-rearranged and ETV6-RUNX1) and novel subtypes of B-ALL (e.g., NUTM1 and ETV6-RUNX1-like). A high mRNA for IGF2BP3 was associated with a proliferative “metagene” signature and a high expression of CDK6 in B-ALL. A low expression portended inferior survival in a high-risk cohort of pediatric B-ALL. Overall, our results show that IGF2BP3 shows subtype-specificity in expression and provides prognostic utility in high-risk B-ALL.