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PDF File - (58K) - Secondary screen identified 168 GSC -effective compounds with selective inhibition profile

Glioblastoma (GBM) is among the most lethal of all cancers. GBM consist of a heterogeneous population of tumor cells among which a tumor-initiating and treatment-resistant subpopulation, here termed GBM stem cells, have been identified as primary therapeutic targets. Here, we describe a high-throughput small molecule screening approach that enables the identification and characterization of chemical compounds that are effective against GBM stem cells. The paradigm uses a tissue culture model to enrich for GBM stem cells derived from human GBM resections and combines a phenotype-based screen with gene target-specific screens for compound identification. We used 31,624 small molecules from 7 chemical libraries that we characterized and ranked based on their effect on a panel of GBM stem cell-enriched cultures and their effect on the expression of a module of genes whose expression negatively correlates with clinical outcome: MELK, ASPM, TOP2A, and FOXM1b. Of the 11 compounds meeting criteria for exerting differential effects across cell types used, 4 compounds showed selectivity by inhibiting multiple GBM stem cells-enriched cultures compared with nonenriched cultures: emetine, n-arachidonoyl dopamine, n-oleoyldopamine (OLDA), and n-palmitoyl dopamine. ChemBridge compounds #5560509 and #5256360 inhibited the expression of the 4 mitotic module genes. OLDA, emetine, and compounds #5560509 and #5256360 were chosen for more detailed study and inhibited GBM stem cells in self-renewal assays in vitro and in a xenograft model in vivo. These studies show that our screening strategy provides potential candidates and a blueprint for lead compound identification in larger scale screens or screens involving other cancer types. Mol Cancer Ther; 10(10); 1818–28. ©2011 AACR.

PDF File - (32K) - GSC-enriched GBM samples form tumors after transplantation into immunodeficient mice

PDF File - (874K) - A - Representative images demonstrating tumor formation success in an animal transplanted with serum (I) and sphere (II) derived GBM146 Ki67 and Human Nuclei (HuNu) immunostaining shows that virtually all transplanted cells died off in the animal transplanted with serum derived cells while an infiltrating tumor mass is visible in the animal transplanted with sphere derived cells of the same GBM sample (i - injection site, V- ventricle, SVZ - subventricular zone). B - Immunohistological sections for Human Nuclei (HuNu) and Ki67 demonstrating good survival of transplanted cells in animals that were sacrificed on the same day as well as 48h after transplantation (IT- injection tract, ST-striatum).

PDF File - (67K) - Effects of OLDA, PALDA and NADA are not mediated via cannabinoid, vanilloid or dopamine receptors A - Exposure of GSC -enriched GBM 157 cells to CB1, CB2, TRPV1 receptor agonists or their combination, does not reproduce the phenotype seen after exposure to OLDA, PALDA or NADA. Graph showing ATP level expressed as % of DMSO treated controls. B - Exposure of OLDA, PALDA or NADA-pretreated GSC -enriched GBM 157 cells (at IC50 concentration) to antagonists of CB, TRPV and D receptors does not rescue the phenotype caused by OLDA, PALDA or NADA (B, C and D, respectively). The graphs are showing ATP levels expressed as % of the DMSO treated controls (100%). SE shown.

PDF File - (61K) - OLDA, Emetine, 5256360 and 5560509 decrease clonal sphere formation in low passage GBM312 Graphs show a concentration-dependent reduction of clonal neurosphere size, neurosphere number as well as total cell mass. Error bars represent SEM.

PDF File - (39K) - Limiting dilution transplantation of GBM157 cells treated with OLDA, 5256360 or 5560509 shows decreased tumor formation frequency

PDF File - (523K) - OLDA, Emetine, 5256360 and 5560509 inhibit proliferation of sphere cultured GBM312 cells, using CFSE (carboxyfluorescein diacetate, succinimidyl ester) cell tracing experiments Proliferative inhibition is graphically represented by an increase in fluorescence or a shift to the right, away from that of the control. A proliferation index (PI) is a numerical value generated for each sample that measures the total increase in cell number divided by the original parent number. PI's are expressed as a percent of control: Emetine (0.2�M)- 18.6%, OLDA (4�M)- 16.0%, Compound 33 (2�M)- 31%, and Compound 62 (2�M)- 24.3%.

PDF File - (40K) - Growth kinetics of serum and sphere derived cells in screening (sphere) media reveals only minor differences in the degree of growth/proliferation between sphere and serum derived cultures of the same cell type Graph shows results of an ATP-based proliferation assay as measured 3 days after cell plating. Error bars depict SEM.

PDF File - (55K) - Sensitivity of GBM cells to OLDA, PALDA and NADA changes as a function of time, for which the cells were cultured in GSC -enriching media GBM107 cells were exposed to GSC -enriching media for increasing amounts of time and were then screened for sensitivity to dopaminergic cannabinoid compounds. While screening in serum-containing media requires a higher compound concentration due to the protective effect of serum in general, serum-derived cells become more and more sensitive towards GSC -specific compounds over time after transfer into sphere media (A, B, C). Such a difference is not present using non-GSC selective chemotherapeutics (D) or non-GBM control cells (E, F). ('ser in ser': serum derived cells screened in serum media; 'ser in sph': serum derived cells screened in sphere media; '1d sph': serum derived cells were cultured in sphere media for one day before the screen, etc.)

Summary In this article we develop a method based on model-X knockoffs to find conditional associations that are consistent across environments, while controlling the false discovery rate. The motivation for this problem is that large datasets may contain numerous associations that are statistically significant and yet misleading, as they are induced by confounders or sampling imperfections. However, associations replicated under different conditions may be more interesting. In fact, sometimes consistency provably leads to valid causal inferences even if conditional associations do not. Although the proposed method is widely applicable, in this paper we highlight its relevance to genome-wide association studies, in which robustness across populations with diverse ancestries mitigates confounding due to unmeasured variants. The effectiveness of this approach is demonstrated by simulations and applications to UK Biobank data.

This paper presents and compares alternative transfer learning methods that can increase the power of conditional testing via knockoffs by leveraging prior information in external data sets collected from different populations or measuring related outcomes. The relevance of this methodology is explored in particular within the context of genome-wide association studies, where it can be helpful to address the pressing need for principled ways to suitably account for, and efficiently learn from the genetic variation associated to diverse ancestries. Finally, we apply these methods to analyze several phenotypes in the UK Biobank data set, demonstrating that transfer learning helps knockoffs discover more associations in the data collected from minority populations, potentially opening the way to the development of more accurate polygenic risk scores.

BackgroundThe identification of geographic variation in incidence can be an important step in the delineation of disease risk factors, but has mostly been undertaken in upper-income countries. Here, we use Electronic Health Records (EHR) from a middle-income country, Colombia, to characterize geographic variation in major mental disorders.MethodWe leveraged geolocated EHRs of 16,295 patients at a psychiatric hospital serving the entire state of Caldas, all of whom received a primary diagnosis of bipolar disorder, schizophrenia, or major depressive disorder at their first visit. To identify the relationship between travel time and incidence of mental illness we used a zero-inflated negative binomial regression model. We used spatial scan statistics to identify clusters of patients, stratified by diagnosis and severity: mild (outpatients) or severe (inpatients).ResultsWe observed a significant association between incidence and travel time for outpatients (N = 11,077, relative risk (RR) = 0.80, 95% confidence interval (0.71, 0.89)), but not inpatients (N = 5,218). We found seven clusters of severe mental illness: the cluster with the most extreme overrepresentation of bipolar disorder (RR = 5.83, p < 0.001) has an average annual incidence of 8.7 inpatients per 10,000 residents, among the highest frequencies worldwide.ConclusionsThe hospital database reflects the geographic distribution of severe, but not mild, mental illness within Caldas. Each hotspot is a candidate location for further research to identify genetic or environmental risk factors for severe mental illness. Our analyses highlight how existing infrastructure from middle-income countries can be extraordinary resources for population studies.

Summary We introduce a multiple testing procedure that controls global error rates at multiple levels of resolution. Conceptually, we frame this problem as the selection of hypotheses that are organized hierarchically in a tree structure. We describe a fast algorithm and prove that it controls relevant error rates given certain assumptions on the dependence between the $p$-values. Through simulations, we demonstrate that the proposed procedure provides the desired guarantees under a range of dependency structures and that it has the potential to gain power over alternative methods. Finally, we apply the method to studies on the genetic regulation of gene expression across multiple tissues and on the relation between the gut microbiome and colorectal cancer.

Significance The goal of genome-wide association studies is to identify meaningful relationships between genotypes and outcomes of interest. One challenge in the analysis of genetic data is that not all true statistical associations represent relevant biological activity; irrelevant but true associations can arise from the confounding effect of environmental conditions or other factors. We propose a method to analyze such data that is immune to this problem because it uses the variation in inheritance as a randomized experiment. The method can leverage any machine-learning algorithm as well as findings from other studies., We introduce a method to draw causal inferences—inferences immune to all possible confounding—from genetic data that include parents and offspring. Causal conclusions are possible with these data because the natural randomness in meiosis can be viewed as a high-dimensional randomized experiment. We make this observation actionable by developing a conditional independence test that identifies regions of the genome containing distinct causal variants. The proposed digital twin test compares an observed offspring to carefully constructed synthetic offspring from the same parents to determine statistical significance, and it can leverage any black-box multivariate model and additional nontrio genetic data to increase power. Crucially, our inferences are based only on a well-established mathematical model of recombination and make no assumptions about the relationship between the genotypes and phenotypes. We compare our method to the widely used transmission disequilibrium test and demonstrate enhanced power and localization.

To personalize treatment for renal cell carcinoma (RCC), it would be ideal to confirm the activity of druggable protein pathways within individual tumors. We have developed a high-resolution nanoimmunoassay (NIA) to measure protein activity with high precision in scant specimens (eg, fine needle aspirates [FNAs]). Here, we used NIA to determine whether protein activation varied in different regions of RCC tumors. Since most RCC therapies target angiogenesis by inhibiting the vascular endothelial growth factor (VEGF) receptor, we quantified phosphorylation of extracellular signal-regulated kinase (ERK), a downstream effector of the VEGF signaling pathway. In 90 ex vivo FNA biopsies sampled from multiple regions of 38 primary clear cell RCC tumors, ERK phosphorylation differed among patients. In contrast, within individual patients, we found limited intratumoral heterogeneity of ERK phosphorylation. Our results suggest that measuring ERK in a single FNA may be representative of ERK activity in different regions of the same tumor. As diagnostic and therapeutic protein biomarkers are being sought, NIA measurements of protein signaling may increase the clinical utility of renal mass biopsy and allow for the application of precision oncology for patients with localized and advanced RCC. Patient summary In this report, we applied a new approach to measure the activity of extracellular signal-regulated kinase (ERK), a key cancer signaling protein, in different areas within kidney cancers. We found that ERK activity varied between patients, but that different regions within individual kidney tumors showed similar ERK activity. This suggests that a single biopsy of renal cell carcinoma may be sufficient to measure protein signaling activity to aid in precision oncology approaches.

Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance using empirical simulations and two applications: (1) analysis of 1,403 binary phenotypes from the UK Biobank data in 408,961 samples of European ancestry, and (2) a meta-analysis for Alzheimer’s disease (AD) comprising nine overlapping large-scale GWAS, whole-exome and whole-genome sequencing studies. The UK Biobank analysis demonstrates superior performance of the proposed method compared to conventional GWAS in both statistical power (2.05-fold more discoveries) and localization of putative causal variants at each locus (46% less proxy variants due to linkage disequilibrium). The AD meta-analysis identified 55 risk loci (including 31 new loci) with ~70% of the proximal genes at these loci showing suggestive signal in downstream single-cell transcriptomic analyses. Our results demonstrate that GhostKnockoff can identify putatively functional variants with weaker statistical effects that are missed by conventional association tests.

Significance Genome-wide association studies compare a phenotype to thousands of genetic variants, searching for associations of potential biological interest. Standard analyses rely on linear models of the phenotype given one variable at a time. However, their assumptions are difficult to verify and their univariate approaches make it hard to recognize interesting associations from spurious ones. Our work takes a different path: We analyze all variants simultaneously, modelling the randomness in the genotypes, which is better understood, instead of the phenotype. Our solution accounts for linkage disequilibrium and population structure, controls the false discovery rate, and leverages powerful machine-learning tools. Applications to the UK Biobank data indicate increased power compared to state-of-the-art alternatives and high replicability., We present a comprehensive statistical framework to analyze data from genome-wide association studies of polygenic traits, producing interpretable findings while controlling the false discovery rate. In contrast with standard approaches, our method can leverage sophisticated multivariate algorithms but makes no parametric assumptions about the unknown relation between genotypes and phenotype. Instead, we recognize that genotypes can be considered as a random sample from an appropriate model, encapsulating our knowledge of genetic inheritance and human populations. This allows the generation of imperfect copies (knockoffs) of these variables that serve as ideal negative controls, correcting for linkage disequilibrium and accounting for unknown population structure, which may be due to diverse ancestries or familial relatedness. The validity and effectiveness of our method are demonstrated by extensive simulations and by applications to the UK Biobank data. These analyses confirm our method is powerful relative to state-of-the-art alternatives, while comparisons with other studies validate most of our discoveries. Finally, fast software is made available for researchers to analyze Biobank-scale datasets.

Structural variation in the complement 4 gene (C4) confers genetic risk for schizophrenia. The variation includes numbers of the increased C4A copy number, which predicts increased C4A mRNA expression. C4-anaphylatoxin (C4-ana) is a C4 protein fragment released upon C4 protein activation that has the potential to change the blood–brain barrier (BBB). We hypothesized that elevated plasma levels of C4-ana occur in individuals with schizophrenia (iSCZ). Blood was collected from 15 iSCZ with illness duration < 5 years and from 14 healthy controls (HC). Plasma C4-ana was measured by radioimmunoassay. Other complement activation products C3-ana, C5-ana, and terminal complement complex (TCC) were also measured. Digital-droplet PCR was used to determine C4 gene structural variation state. Recombinant C4-ana was added to primary brain endothelial cells (BEC) and permeability was measured in vitro. C4-ana concentration was elevated in plasma from iSCZ compared to HC (mean = 654 ± 16 ng/mL, 557 ± 94 respectively, p = 0.01). The patients also carried more copies of the C4AL gene and demonstrated a positive correlation between plasma C4-ana concentrations and C4A gene copy number. Furthermore, C4-ana increased the permeability of a monolayer of BEC in vitro. Our findings are consistent with a specific role for C4A protein in schizophrenia and raise the possibility that its activation product, C4-ana, increases BBB permeability. Exploratory analyses suggest the novel hypothesis that the relationship between C4-ana levels and C4A gene copy number could also be altered in iSCZ, suggesting an interaction with unknown genetic and/or environmental risk factors.

Background Molecular and cellular changes are intrinsic to aging and age-related diseases. Prior cross-sectional studies have investigated the combined effects of age and genetics on gene expression and alternative splicing; however, there has been no long-term, longitudinal characterization of these molecular changes, especially in older age. Results We perform RNA sequencing in whole blood from the same individuals at ages 70 and 80 to quantify how gene expression, alternative splicing, and their genetic regulation are altered during this 10-year period of advanced aging at a population and individual level. We observe that individuals are more similar to their own expression profiles later in life than profiles of other individuals their own age. We identify 1291 and 294 genes differentially expressed and alternatively spliced with age, as well as 529 genes with outlying individual trajectories. Further, we observe a strong correlation of genetic effects on expression and splicing between the two ages, with a small subset of tested genes showing a reduction in genetic associations with expression and splicing in older age. Conclusions These findings demonstrate that, although the transcriptome and its genetic regulation is mostly stable late in life, a small subset of genes is dynamic and is characterized by a reduction in genetic regulation, most likely due to increasing environmental variance with age.

Summary The goal of expression quantitative trait loci (eQTL) studies is to identify the genetic variants that influence the expression levels of the genes in an organism. High throughput technology has made such studies possible: in a given tissue sample, it enables us to quantify the expression levels of approximately 20 000 genes and to record the alleles present at millions of genetic polymorphisms. While obtaining this data is relatively cheap once a specimen is at hand, obtaining human tissue remains a costly endeavor: eQTL studies continue to be based on relatively small sample sizes, with this limitation particularly serious for tissues as brain, liver, etc.—often the organs of most immediate medical relevance. Given the high-dimensional nature of these datasets and the large number of hypotheses tested, the scientific community has adopted early on multiplicity adjustment procedures. These testing procedures primarily control the false discoveries rate for the identification of genetic variants with influence on the expression levels. In contrast, a problem that has not received much attention to date is that of providing estimates of the effect sizes associated with these variants, in a way that accounts for the considerable amount of selection. Yet, given the difficulty of procuring additional samples, this challenge is of practical importance. We illustrate in this work how the recently developed conditional inference approach can be deployed to obtain confidence intervals for the eQTL effect sizes with reliable coverage. The procedure we propose is based on a randomized hierarchical strategy with a 2-fold contribution: (1) it reflects the selection steps typically adopted in state of the art investigations and (2) it introduces the use of randomness instead of data-splitting to maximize the use of available data. Analysis of the GTEx Liver dataset (v6) suggests that naively obtained confidence intervals would likely not cover the true values of effect sizes and that the number of local genetic polymorphisms influencing the expression level of genes might be underestimated.

Scientific hypotheses in a variety of applications have domain-specific structures, such as the tree structure of the International Classification of Diseases (ICD), the directed acyclic graph structure of the Gene Ontology (GO), or the spatial structure in genome-wide association studies. In the context of multiple testing, the resulting relationships among hypotheses can create redundancies among rejections that hinder interpretability. This leads to the practice of filtering rejection sets obtained from multiple testing procedures, which may in turn invalidate their inferential guarantees. We propose Focused BH, a simple, flexible, and principled methodology to adjust for the application of any pre-specified filter. We prove that Focused BH controls the false discovery rate under various conditions, including when the filter satisfies an intuitive monotonicity property and the p-values are positively dependent. We demonstrate in simulations that Focused BH performs well across a variety of settings, and illustrate this method’s practical utility via analyses of real datasets based on ICD and GO.

Current evidence from case/control studies indicates that genetic risk for psychiatric disorders derives primarily from numerous common variants, each with a small phenotypic impact. The literature describing apparent segregation of bipolar disorder (BP) in numerous multigenerational pedigrees suggests that, in such families, large-effect inherited variants might play a greater role. To identify roles of rare and common variants on BP, we conducted genetic analyses in 26 Colombia and Costa Rica pedigrees ascertained for bipolar disorder 1 (BP1), the most severe and heritable form of BP. In these pedigrees, we performed microarray SNP genotyping of 838 individuals and high-coverage whole-genome sequencing of 449 individuals. We compared polygenic risk scores (PRS), estimated using the latest BP1 genome-wide association study (GWAS) summary statistics, between BP1 individuals and related controls. We also evaluated whether BP1 individuals had a higher burden of rare deleterious single-nucleotide variants (SNVs) and rare copy number variants (CNVs) in a set of genes related to BP1. We found that compared with unaffected relatives, BP1 individuals had higher PRS estimated from BP1 GWAS statistics (P = 0.001 ~ 0.007) and displayed modest increase in burdens of rare deleterious SNVs (P = 0.047) and rare CNVs (P = 0.002 ~ 0.033) in genes related to BP1. We did not observe rare variants segregating in the pedigrees. These results suggest that small-to-moderate effect rare and common variants are more likely to contribute to BP1 risk in these extended pedigrees than a few large-effect rare variants.

We enjoyed reading Professor Efron’s (Brad) paper just as much as we enjoyed listening to his June 2019 lecture in Leiden. One of the core values underlying statistical research is in how it enable...

Go big or you'll get lost Rational mutagenesis is a common approach to investigating or engineering enzyme function in vitro, but the ease with which one can manipulate protein sequences belies many pitfalls in connecting sparse activity data to an enzyme's true functional landscape. Using a high-throughput platform, Markin et al. expressed, purified, and performed an array of kinetic measurements on a target esterase, collecting data from >1000 mutations spanning the entire protein (see the Perspective by Baumer and Whitehead). Protein misfolding into an inactive state, rather than decreased equilibrium stability, was a crucial factor in negatively affected variants spread throughout the protein. When combined with prior mechanistic understanding and structures, four “functional components” help to rationalize the otherwise complex spatial pattern of effects of mutations on different aspects of enzyme function, all of which would be invisible from mutagenesis of just a few residues. Science , abf8761, this issue p. eabf8761 ; see also abj8346, p. 391

The Genotype-Tissue Expression (GTEx) project dissects how genetic variation affects gene expression and splicing. Some human genetic variants affect the amount of RNA produced and the splicing of gene transcripts, crucial steps in development and maintaining a healthy individual. However, some of these changes only occur in a small number of tissues within the body. The Genotype-Tissue Expression (GTEx) project has been expanded over time, and, looking at the final data in version 8, Aguet et al. present a deep characterization of genetic associations and gene expression and splicing in 838 individuals over 49 tissues (see the Perspective by Wilson). This large study was able to characterize the details underlying many aspects of gene expression and provides a resource with which to better understand the fundamental molecular mechanisms of how genetic variants affect gene regulation and complex traits in humans. Science, this issue p. 1318; see also p. 1298 The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues. We thank the donors and their families for their generous gifts of organ donation for transplantation and tissue donations for the GTEx research project; the Genomics Platform at the Broad Institute for data generation; J. Struewing for support and leadership of the GTEx project; M. Khan and C. Stolte for the illustrations in Fig. 1; and R. Do, D. Jordan, and M. Verbanck for providing GWAS pleiotropy scores. Funding: This work was supported by the Common Fund of the Office of the Director, U.S. National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, NIA, NIAID, and NINDS through NIH contracts HHSN261200800001E (Leidos Prime contract with NCI: A.M.S., D.E.T., N.V.R., J.A.M., L.S., M.E.B., L.Q., T.K., D.B., K.R., and A.U.), 10XS170 (NDRI: W.F.L., J.A.T., G.K., A.M., S.S., R.H., G.Wa., M.J., M.Wa., L.E.B., C.J., J.W., B.R., M.Hu., K.M., L.A.S., H.M.G., M.Mo., and L.K.B.), 10XS171 (Roswell Park Cancer Institute: B.A.F., M.T.M., E.K., B.M.G., K.D.R., and J.B.), 10X172 (Science Care Inc.), 12ST1039 (IDOX), 10ST1035 (Van Andel Institute: S.D.J., D.C.R., and D.R.V.), HHSN268201000029C (Broad Institute: F.A., G.G., K.G.A., A.V.S., X.Li., E.T., S.G., A.G., S.A., K.H.H., D.T.N., K.H., S.R.M., and J.L.N.), 5U41HG009494 (F.A., G.G., and K.G.A.), and through NIH grants R01 DA006227-17 (University of Miami Brain Bank: D.C.M. and D.A.D.), Supplement to University of Miami grant DA006227 (D.C.M. and D.A.D.), R01 MH090941 (University of Geneva), R01 MH090951 and R01 MH090937 (University of Chicago), R01 MH090936 (University of North Carolina–Chapel Hill), R01MH101814 (M.M.-A., V.W., S.B.M., R.G., E.T.D., D.G.-M., and A.V.), U01HG007593 (S.B.M.), R01MH101822 (C.D.B.), U01HG007598 (M.O. and B.E.S.), U01MH104393 (A.P.F.), extension H002371 to 5U41HG002371 (W.J.K.), as well as other funding sources: R01MH106842 (T.L., P.M., E.F., and P.J.H.), R01HL142028 (T.L., Si.Ka., and P.J.H.), R01GM122924 (T.L. and S.E.C.), R01MH107666 (H.K.I.), P30DK020595 (H.K.I.), UM1HG008901 (T.L.), R01GM124486 (T.L.), R01HG010067 (Y.Pa.), R01HG002585 (G.Wa. and M.St.), Gordon and Betty Moore Foundation GBMF 4559 (G.Wa. and M.St.), 1K99HG009916-01 (S.E.C.), R01HG006855 (Se.Ka. and R.E.H.), BIO2015-70777-P, Ministerio de Economia y Competitividad and FEDER funds (M.M.-A., V.W., R.G., and D.G.-M.), la Caixa Foundation ID 100010434 under agreement LCF/BQ/SO15/52260001 (D.G.-M.), NIH CTSA grant UL1TR002550-01 (P.M.), Marie-Skłodowska Curie fellowship H2020 Grant 706636 (S.K.-H.), R35HG010718 (E.R.G.), FPU15/03635, Ministerio de Educación, Cultura y Deporte (M.M.-A.),R01MH109905, 1R01HG010480 (A.Ba.), Searle Scholar Program (A.Ba.), R01HG008150 (S.B.M.), 5T32HG000044-22, NHGRI Institutional Training Grant in Genome Science (N.R.G.), EU IMI program (UE7-DIRECT-115317-1) (E.T.D. and A.V.), FNS funded project RNA1 (31003A_149984) (E.T.D. and A.V.), DK110919 (F.H.), F32HG009987 (F.H.), Massachusetts Lions Eye Research Fund Grant (A.R.H.), Wellcome grant WT108749/Z/15/Z (P.F.), and European Molecular Biology Laboratory (P.F. and D.Z.). Peer Reviewed "Article signat per 1 autors/es del BSC membres del THE GTEX CONSORTIUM: Marta Mele Messeguer"

We propose GLISS, a strategy to discover spatially-varying genes by integrating two data sources: (1) spatial gene expression data such as image-based fluorescence in situ hybridization techniques, and (2) dissociated whole-transcriptome single-cell RNA-sequencing (scRNA-seq) data. GLISS utilizes a graph-based association measure to select and link genes that are spatially-dependent in both data sources. GLISS can discover new spatial genes and recover cell locations in scRNA-seq data from landmark genes determined from SGE data. GLISS also offers a new dimension reduction technique to cluster the genes, while accounting for the inferred spatial structure of the cells. We demonstrate the utility of GLISS on simulated and real datasets, including datasets on the mouse olfactory bulb and breast cancer biopsies, and two spatial studies of the mammalian liver and intestine.

The distal lung contains terminal bronchioles and alveoli that facilitate gas exchange and is affected by disorders including interstitial lung disease, cancer, and SARS-CoV-2-associated COVID-19 pneumonia. Investigations of these localized pathologies have been hindered by a lack of 3D in vitro human distal lung culture systems. Further, human distal lung stem cell identification has been impaired by quiescence, anatomic divergence from mouse and lack of lineage tracing and clonogenic culture. Here, we developed robust feeder-free, chemically-defined culture of distal human lung progenitors as organoids derived clonally from single adult human alveolar epithelial type II (AT2) or KRT5+ basal cells. AT2 organoids exhibited AT1 transdifferentiation potential, while basal cell organoids progressively developed lumens lined by differentiated club and ciliated cells. Organoids consisting solely of club cells were not observed. Upon single cell RNA-sequencing (scRNA-seq), alveolar organoids were composed of proliferative AT2 cells; however, basal organoid KRT5+ cells contained a distinct ITGA6+ITGB4+ mitotic population whose proliferation segregated to a TNFRSF12Ahi subfraction. Clonogenic organoid growth was markedly enriched within the TNFRSF12Ahi subset of FACS-purified ITGA6+ITGB4+ basal cells from human lung or derivative organoids. In vivo, TNFRSF12A+ cells comprised ~10% of KRT5+ basal cells and resided in clusters within terminal bronchioles. To model COVID-19 distal lung disease, we everted the polarity of basal and alveolar organoids to rapidly relocate differentiated club and ciliated cells from the organoid lumen to the exterior surface, thus displaying the SARS-CoV-2 receptor ACE2 on the outwardly-facing apical aspect. Accordingly, basal and AT2 “apical-out” organoids were infected by SARS-CoV-2, identifying club cells as a novel target population. This long-term, feeder-free organoid culture of human distal lung alveolar and basal stem cells, coupled with single cell analysis, identifies unsuspected basal cell functional heterogeneity and exemplifies progenitor identification within a slowly proliferating human tissue. Further, our studies establish a facile in vitro organoid model for human distal lung infectious diseases including COVID-19-associated pneumonia.