Proportions Of Cell Types Research Articles

Predicting Algorithm of Tissue Cell Ratio Based on Deep Learning Using Single-Cell RNA Sequencing

Background: Understanding the proportion of cell types in heterogeneous tissue samples is important in bioinformatics. It is a challenge to infer the proportion of tissues using bulk RNA sequencing data in bioinformatics because most traditional algorithms for predicting tissue cell ratios heavily rely on standardized specific cell-type gene expression profiles, and do not consider tissue heterogeneity. The prediction accuracy of algorithms is limited, and robustness is lacking. This means that new approaches are needed urgently. Methods: In this study, we introduced an algorithm that automatically predicts tissue cell ratios named Autoptcr. The algorithm uses the data simulated by single-cell RNA sequencing (ScRNA-Seq) for model training, using convolutional neural networks (CNNs) to extract intrinsic relationships between genes and predict the cell proportions of tissues. Results: We trained the algorithm using simulated bulk samples and made predictions using real bulk PBMC data. Comparing Autoptcr with existing advanced algorithms, the Pearson correlation coefficient between the actual value of Autoptcr and the predicted value was the highest, reaching 0.903. Tested on a bulk sample, the correlation coefficient of Lin was 41% higher than that of CSx. The algorithm can infer tissue cell proportions directly from tissue gene expression data. Conclusions: The Autoptcr algorithm uses simulated ScRNA-Seq data for training to solve the problem of specific cell-type gene expression profiles. It also has high prediction accuracy and strong noise resistance for the tissue cell ratio. This work is expected to provide new research ideas for the prediction of tissue cell proportions.

1277-P: Spatial Whole Transcriptome Profiling of the Human Pancreas Uncovers Unique Insights into Histological Structures

Understanding the physiology of the pancreas and pathology that occurs during diabetes requires knowing transcriptional patterns driving biological activity within the functional structures of the tissue. Previous technologies have not enabled spatially resolved high plex profiling to elucidate these biological pathways. Using the unique ability of GeoMx® Digital Spatial Profiler to resolve functional units within FFPE tissue in situ, we provide whole transcriptome expression data from islets, acini, and ducts from four pancreata of nondiabetes patients. On average, 6000-8000 genes were detected from functional groups within each histological structure. Data were validated against the independent Human Protein Atlas, with ∼90% concordance between RNA expression and known protein localization. Deconvolution based on single-cell RNASeq enabled calculation of the proportion of distinct cell types within each structure, confirming cell composition. For example, while ductal components consist of small islands punctuating the acini, over 3000 genes were identified to be highly specific to each structure (FDR < 0.05) , and enriched for pathways related to focal adhesion, ECM structure, and diabetic disease processes. Within islets, we identified >300 genes with differences between α- and β-cells (FDR < 0.2) . Functional characterization confirmed enrichment of insulin biosynthesis and secretion pathways, with genes such as insulin, glucagon, and IAPP expression captured accurately with a single assay. Determination of expression in four individual pancreas samples identified variation between tissues, which may be central to health and disease processes. In conclusion, whole transcriptome data for spatial gene expression profiles of structures of the non-diseased pancreas are publicly available and can be used as standards to inform future profiling studies in normal and diseased tissue. FOR RESEARCH USE ONLY. Not for use in diagnostic procedures. Disclosure W.Yang: None. M.F.Hogan: Employee; NanoString. Y.Liang: None. J.M.Beechem: None.

Advantages and Challenges of Differential Immune Cell Count Determination in Blood and Milk for Monitoring the Health and Well-Being of Dairy Cows

A key challenge of the 21st century will be to provide the growing world population with a sustainable and secure supply of food. Consequently, the dairy farming’s primary task is to lower milk losses and other inefficiencies associated with diseased cows. Moreover, a shift from curative to preventive health management would be desirable for mastitis and a wide variety of other infectious and non-infectious cattle diseases, some of which are known to have profound negative effects on the performance and well-being of cows. Differential cell counting (DCC), a procedure that aims to determine the proportions of different somatic cell types in raw milk samples, has not only the potential to optimize mastitis diagnostics, but it could furthermore serve as a diagnostic tool for monitoring the general and overall health status of dairy cows. Based on a broad search of the literature, the practical utility of various types of DCC is summarized and discussed in this review. Since it might be of advantage to interpret DCC with the aid of data from studies in humans, differences between the immune systems of humans and dairy cattle, with a special focus on surface marker expression profiles and γδ (gamma delta) T-cell characteristics, are also described.

ACE2 expression in adipose tissue is associated with cardio-metabolic risk factors and cell type composition—implications for COVID-19

BackgroundCOVID-19 severity varies widely. Although some demographic and cardio-metabolic factors, including age and obesity, are associated with increasing risk of severe illness, the underlying mechanism(s) are uncertain.Subjects/methodsIn a meta-analysis of three independent studies of 1471 participants in total, we investigated phenotypic and genetic factors associated with subcutaneous adipose tissue expression of Angiotensin I Converting Enzyme 2 (ACE2), measured by RNA-Seq, which acts as a receptor for SARS-CoV-2 cellular entry.ResultsLower adipose tissue ACE2 expression was associated with multiple adverse cardio-metabolic health indices, including type 2 diabetes (T2D) (P = 9.14 × 10−6), obesity status (P = 4.81 × 10−5), higher serum fasting insulin (P = 5.32 × 10−4), BMI (P = 3.94 × 10−4), and lower serum HDL levels (P = 1.92 × 10−7). ACE2 expression was also associated with estimated proportions of cell types in adipose tissue: lower expression was associated with a lower proportion of microvascular endothelial cells (P = 4.25 × 10−4) and higher proportion of macrophages (P = 2.74 × 10−5). Despite an estimated heritability of 32%, we did not identify any proximal or distal expression quantitative trait loci (eQTLs) associated with adipose tissue ACE2 expression.ConclusionsOur results demonstrate that individuals with cardio-metabolic features known to increase risk of severe COVID-19 have lower background ACE2 levels in this highly relevant tissue. Reduced adipose tissue ACE2 expression may contribute to the pathophysiology of cardio-metabolic diseases, as well as the associated increased risk of severe COVID-19.

Dynamic human liver proteome atlas reveals functional insights into disease pathways

Deeper understanding of liver pathophysiology would benefit from a comprehensive quantitative proteome resource at cell type resolution to predict outcome and design therapy. Here, we quantify more than 150,000 sequence‐unique peptides aggregated into 10,000 proteins across total liver, the major liver cell types, time course of primary cell cultures, and liver disease states. Bioinformatic analysis reveals that half of hepatocyte protein mass is comprised of enzymes and 23% of mitochondrial proteins, twice the proportion of other liver cell types. Using primary cell cultures, we capture dynamic proteome remodeling from tissue states to cell line states, providing useful information for biological or pharmaceutical research. Our extensive data serve as spectral library to characterize a human cohort of non‐alcoholic steatohepatitis and cirrhosis. Dramatic proteome changes in liver tissue include signatures of hepatic stellate cell activation resembling liver cirrhosis and providing functional insights. We built a web‐based dashboard application for the interactive exploration of our resource (www.liverproteome.org).

Growth and differentiation of human induced pluripotent stem cell (hiPSC) derived kidney organoids using fully synthetic peptide hydrogels

Human induced pluripotent stem cell (hiPSC)‐derived kidney organoids have prospective applications ranging from basic disease modelling to personalised medicine. However, there remains a necessity to refine the biophysical and biochemical parameters that govern kidney organoid formation. Differentiation within fully‐controllable and physiologically relevant 3D growth environments will be critical to improving organoid reproducibility and maturation. Here, we matured hiPSC‐derived kidney organoids within fully synthetic self‐assembling peptide hydrogels (SAPHs) of variable stiffness (storage modulus, G′). The resulting organoids contained complex structures comparable to those differentiated within the animal‐derived matrix, Matrigel. Single‐cell RNA sequencing (scRNA‐seq) was then used to compare organoids matured within SAPHs to those grown within Matrigel or at the air‐liquid interface. A total of 13,179 cells were analysed, revealing 14 distinct clusters. Organoid compositional analysis revealed a larger proportion of nephron cell types within Transwell‐derived organoids, while SAPH derived organoids were enriched for stromal‐associated cell populations. Notably, differentiation within a higher G’ SAPH generated podocytes with more mature gene expression profiles. Additionally, maturation within a 3D microenvironment significantly reduced the derivation of offtarget cell types, which are a known limitation of current kidney organoid protocols. This work demonstrates the utility of synthetic peptide‐based hydrogels with a defined stiffness, as a minimally complex microenvironment for the selected differentiation of kidney organoids.

Cysteinyl–leukotriene and prostaglandin pathways in bronchial versus alveolar lavage in allergic asthmatics

Cysteinyl–leukotriene and prostaglandin pathways in bronchial versus alveolar lavage in allergic asthmatics

DestVI identifies continuums of cell types in spatial transcriptomics data.

Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools ( https://scvi-tools.org ).

DevKidCC allows for robust classification and direct comparisons of kidney organoid datasets

BackgroundWhile single-cell transcriptional profiling has greatly increased our capacity to interrogate biology, accurate cell classification within and between datasets is a key challenge. This is particularly so in pluripotent stem cell-derived organoids which represent a model of a developmental system. Here, clustering algorithms and selected marker genes can fail to accurately classify cellular identity while variation in analyses makes it difficult to meaningfully compare datasets. Kidney organoids provide a valuable resource to understand kidney development and disease. However, direct comparison of relative cellular composition between protocols has proved challenging. Hence, an unbiased approach for classifying cell identity is required.MethodsThe R package, scPred, was trained on multiple single cell RNA-seq datasets of human fetal kidney. A hierarchical model classified cellular subtypes into nephron, stroma and ureteric epithelial elements. This model, provided in the R package DevKidCC (github.com/KidneyRegeneration/DevKidCC), was then used to predict relative cell identity within published kidney organoid datasets generated using distinct cell lines and differentiation protocols, interrogating the impact of such variations. The package contains custom functions for the display of differential gene expression within cellular subtypes.ResultsDevKidCC was used to directly compare between distinct kidney organoid protocols, identifying differences in relative proportions of cell types at all hierarchical levels of the model and highlighting variations in stromal and unassigned cell types, nephron progenitor prevalence and relative maturation of individual epithelial segments. Of note, DevKidCC was able to distinguish distal nephron from ureteric epithelium, cell types with overlapping profiles that have previously confounded analyses. When applied to a variation in protocol via the addition of retinoic acid, DevKidCC identified a consequential depletion of nephron progenitors.ConclusionsThe application of DevKidCC to kidney organoids reproducibly classifies component cellular identity within distinct single-cell datasets. The application of the tool is summarised in an interactive Shiny application, as are examples of the utility of in-built functions for data presentation. This tool will enable the consistent and rapid comparison of kidney organoid protocols, driving improvements in patterning to kidney endpoints and validating new approaches.

Abstract P2-08-08: Concurrent predictors of an immune responsive tumor microenvironment within tumor mutational burden-high breast cancer

Abstract Background: Data supporting high tumor mutational burden (TMB-H) as a lone biomarker for an immune responsive tumor microenvironment (TME) or of response to immune checkpoint inhibition (ICI) in metastatic breast cancer (MBC) is weak; yet the tumor agnostic approval in TMB-H advanced tumors makes ICI an option in the clinic. PDL-1 positivity (PDL-1+) is the only consistent biomarker for ICI benefit in metastatic triple-negative BC (mTNBC). We sought to further evaluate concurrent predictors of an immune responsive or non-responsive TME within TMB-H MBC. Methods: Tumor samples (N=5621) obtained from patients with MBC were analyzed by next-generation sequencing (NGS) of DNA (592-gene panel or whole exome sequencing) and RNA (whole transcriptome sequencing) at Caris Life Sciences (Phoenix, AZ). Friends of Cancer Research TMB Harmonization Project (Merino et al., 2020) recommendations, with the TMB-H threshold set to ≥ 10 muts/Mb, were used. PDL-1+ (≥1% immune cells stained) assessed by IHC (Ventana SP142). Deficient-mismatch repair (dMMR) and high microsatellite instability (MSI-H) were determined by IHC and NGS, respectively. Gene expression profiling assessed 21 immune-related signatures and TMEs using MCP-counter (Betch et al., 2016) and quanTIseq (Finotello et al., 2019). Immune cell types were classified as immune-responsive (CD8+ and CD4+ T cells, B cells, myeloid dendritic cells, M1 macrophages, NK cells, and neutrophils) or non-responsive (Tregs, M2 macrophages, and monocytes). P-values adjusted by the Benjamini-Hochberg procedure. Exploratory p-values reported for sub-analyses of TMB-H responsive vs nonresponsive TME and PDL1+ vs PDL1- subgroups. Results: TMB-H was identified in 461/5621 (8.2%) MBC samples. TMB-H tumors exhibit significant enrichment of dMMR/MSI-H (7 vs 0%, p<0.0001) and IHC-PDL1+ (36 vs 28%, p<0.05) compared to TMB-L. TMB-H was not associated with increased abundance or proportion of immune responsive cell types or immune response gene signatures (e.g. antigen presentation), yet positive trends were observed. To identify predictive biomarkers of immune responsive (hot) or non-responsive (cold) TME profiles within TMB-H MBC, we generated a composite TME score representing the overall proportion of hot vs cold immune cell types. APOBEC mutagenesis was common in TMB-H tumors (72%) and was more frequently associated with an immune cold TME (77 vs 66% in hot TME subgroup, p=0.068). Within TMB-H tumors, cold TMEs were enriched with mutations (muts) in PIK3CA (61 vs 47%), CHEK2 (5 vs 0%), CBFB (4 vs 0%), amplifications (amp) of CCND1 (13 vs 4%), FGF19 (16 vs 5%), FGF4 (11 vs 4%), and MMLT6 (6 vs 0%), and expression of IHC-AR (75 vs 58%) compared to hot TMEs (all p<0.05). Hot TMEs had increased frequency of MSI-H (12 vs 5%) and KMT2D muts (12 vs 3%) compared to cold TMEs (all p<0.05). Amongst TMB-H and TMB-L tumors, PDL1+ was significantly associated with increased proportion of immune responsive cell types (ie CD8+) compared to PDL1- tumors (0.14 vs -0.08 TME score, p<0.0001). Comparison of TMB-H tumors by PDL1 status revealed enrichment of TP53 (67 vs 53%) and B2M (8 vs 0%) muts in PDL1+ tumors, while IHC-ER positivity (68 vs 54%) and CDH1 muts (32 vs 16%) were increased in PDL1- tumors (all p<0.05). Conclusions: High TMB alone does not strongly correlate with immune infiltrate or immune response gene signatures in MBC. Within TMB-H MBC, concurrent mutations in MSI-H and KMT2D are associated with an immune responsive TME while mutations in PIK3CA, CHEK2, CBFB, amplifications of CCND1, FGF19, FGF4, and MMLT6, and expression of IHC-AR are associated with an immune non-responsive TME. Co-occurring biomarkers within TMB-H breast cancer warrant evaluation in prospective cohorts for response or resistance to ICI to help develop composite biomarkers in breast cancer. Citation Format: Sarah Sammons, Andrew Elliott, Jeremy Force, Saranya Chumsri, Carey Anders, Antoinette R Tan, Daniel Magee, Jia Zeng, W. Michael Korn, Mustafa Kahsraw, Evanthia T Roussos Torres. Concurrent predictors of an immune responsive tumor microenvironment within tumor mutational burden-high breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-08-08.

Abstract P4-01-05: Multi-omics data shows downregulation of mismatch repair, purin and tublin pathways in AR-negative triple-negative chemotherapy-resistant tumors

Abstract Introduction. The standard treatment for non-metastatic triple-negative breast cancer (TNBC) is neoadjuvant chemotherapy (NAC) and nearly 50% exhibit pathological complete response (pCR). However, patients with residual disease after NAC are at increased risk for recurrence and death. Prior studies examining the transcriptome of TNBC pre/post-NAC have examined a limited number of genes (<500) in heterogeneous subgroups of TNBC (e.g. LAR and non-LAR). We explored the transcriptome of androgen-receptor (AR) negative (non-LAR) TNBC subtype both pre/post NAC to identify pathways associated with NAC response. Methods. Tumors obtained pre/post NAC from TNBC patients enrolled in the Breast Cancer Genome Guided therapy study (BEAUTY) underwent RNA sequencing and reverse-phase protein array (RPPA). EdgeR was applied for differentially expressed (DE) analysis and regression methods for RPPA. Digital deconvolution method (CIBERSORTx) and TNBC single-cell data were used to obtain cell types. Pathway analysis was carried out using 2972 gene sets and gene set variation analysis (GSVA). Functional enrichment analysis was conducted with significant genes. Results. Of the 44 TNBC patients, 32 patients were excluded from the analysis cohort due to: LAR tumor (6 pts.), non-LAR tumor with pCR (23 pts.), and cell type issues with RNA-seq data (3 pt.). Paired RNA-Seq data were available for 12 TNBC patients (4 with progression <2 years [EP]) and 8 who were progression-free > 4 years [NP]) and paired RPPA data were available for 9 of these 12 patients. Differentially expressed genes, proteins and cell types between EP and NP in post-NAC. We identified 489 genes differentially expressed (DE) between EP and NP (logFC=|2|, FDR < 0.05). Analysis of cytobands from these 489 genes showed an enrichment of genes on chromosome 6p22.1-2 and 17q25.3 regions (enrichment ratio >5; p-value <10E-4). Critical genes identified in the AR- network (p-value < 10E-3) were IL1RN, SLAMF9, KRT81, BHLHE22, B3GALT5, PCP4, TREM1, AQP9, NRTN, and COL2A1.In addition, preliminary results from RPPA data of post-NAC tumors showed astrocytic phosphoprotein (PEA-15), involved in apoptosis, proliferation, glucose metabolism, as well as cell proliferation and Y box binding (YB1) proteins (involved in metastases), were more DE in EP than NP (p < 0.05). CIBERSORTx was applied to estimate the proportions of different cell types in post-NAC tumors. Cancer-associated fibroblasts iCAFs were low and myCAFs are high in EP vs NP. It is known that the cross-talk between CAFs and tumor cells may induce tumor resistance to chemotherapy. Differentially expressed pathways in post and pre-NAC EP tumors. Using genome-wide expression data from the paired 12 tumors and the GSVA method, we obtained individual pathway scores for 2972 pathways. One hundred ninety pathways were downregulated and 61 pathways were upregulated (p-value <= 0.05) in the post-NAC residual disease of EP relative to NP. We further examined these 190 pathways in the paired EPs and found 71% of those pathways were upregulated in the pre-NAC. These 190 downregulated pathways were enriched with FOXO, TGF-beta, PI3k, FGFR1, insulin and others. The 61 upregulated pathways in post-NAC EP tumors were enriched with mismatch repair, purine, tubulin, telomere, polymerase and gap-junction related pathways; 77% of those 61 pathways were downregulated in pre-NAC. Conclusions. Using a comprehensive “omics” approach, we have identified novel cancer and drug response pathways associated with recurrence in AR-TNBC disease. Further work to evaluate these as markers of outcome and potential drug targets is warranted. Citation Format: Krishna R Kalari, Vera J Suman, Xiaojia Tang, Jason P Sinnwell, Kevin J Thompson, Peter T Vedell, Jodi M Carter, Sarah A McLaughlin, Alvaro Moreno Aspitia, Donald W Northfelt, Richard J Gray, Richard Weinshilboum, Liewei Wang, Judy C Boughey, Matthew Goetz. Multi-omics data shows downregulation of mismatch repair, purin and tublin pathways in AR-negative triple-negative chemotherapy-resistant tumors [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-01-05.

Towards a positive physiological definition of the deep brain nuclei in humans

Using microelectrodes for recording purposes in deep brain stimulation (DBS) has proven to be very useful. Their efficiency can be improved by characterising the properties of extracellular action potentials (EAPs). We analysed the records of nine patients who underwent surgery for epilepsy or aggressiveness under general anaesthesia. The properties of the EAPs of the centromedian, ventral intermediate, ventrocaudal and posteromedial hypothalamic nuclei of the thalamus have been determined. We have analysed 706 thalamic and 142 hypothalamic cells. The proportion of cell types was found to be specific to each cell nucleus. The most frequent cell type was P1P2N1 (59.5%), followed by N1P1N2 (23.1%). The first phase of the EAP is highly variable. The properties of the EAP phases of the same morphology differ greatly from one nucleus to another. We have shown that several deep brain nuclei have properties that are specific to the morphology of the EAPs. This will allow for improved localisation of these nuclei during DBS.

A theoretical understanding of mammalian preimplantation development.

The blastocyst has long been a hallmark system of study in developmental biology due to its importance in mammalian development and clinical relevance for assisted reproductive technologies. In recent years, the blastocyst is emerging as a system of study for mathematical modelling. In this review, we compile, to our knowledge, all models describing preimplantation development. Coupled with experiments, these models have provided insight regarding the morphogenesis and cell-fate specification throughout preimplantation development. In the case of cell-fate specification, theoretical models have provided mechanisms explaining how proportion of cell types are established and maintained when confronted to perturbations. For cell-shape based models, they have described quantitatively how mechanical forces sculpt the blastocyst and even predicted how morphogenesis could be manipulated. As theoretical biology develops, we believe the next critical stage in modelling involves an integration of cell fate and mechanics to provide integrative models of development at distinct spatiotemporal scales. We discuss how, building on a balanced base of mechanical and chemical models, the preimplantation embryo will play a key role in integrating these two faces of the same coin.

Immune Cell Infiltration Analysis Demonstrates Excessive Mast Cell Activation in Psoriasis.

Psoriasis represents multiple inflammatory processes and exaggerated physiological responses to epithelial damage by innate and adaptive immune components, thus it is critical to compare the immune cell niche in disease and healthy skin. Here, we inferred the proportions of different immune cell types in psoriatic and healthy skin using the CIBERSORT algorithm with expression profiles as input. As a result, we observed a dramatic change of immune cell profiles in psoriatic skin compared with healthy skin. Interestingly, the resting mast cells is almost eliminated in psoriatic skin. In contrast, the activated mast cells are enriched in psoriatic skin, indicating that mast cells activation may play an important role in psoriasis pathogenesis. In addition, we found that the proportion of the resting mast cells gradually come back to the normal level in lesioned skin upon etanercept treatment, suggesting that mast cells play a critical role in immune cell niche maintenance. Further experiments validated a significant decrease in mast cell population and an excessive mast cell activation in psoriatic skin compared with healthy skin. In conclusion, our integrative analyses of the immune cell profiles and the corresponding marker genes expression provide a better understanding of the inflammation response in psoriasis and important clues for clinical applications.

A2Sign: Agnostic Algorithms for Signatures-a universal method for identifying molecular signatures from transcriptomic datasets prior to cell-type deconvolution.

Molecular signatures are critical for inferring the proportions of cell types from bulk transcriptomics data. However, the identification of these signatures is based on a methodology that relies on prior biological knowledge of the cell types being studied. When working with less known biological material, a data-driven approach is required to uncover the underlying classes and generate ad hoc signatures from healthy or pathogenic tissue. We present a new approach, A2Sign: Agnostic Algorithms for Signatures, based on a non-negative tensor factorization (NTF) strategy that allows us to identify cell-type-specific molecular signatures, greatly reduce collinearities and also account for inter-individual variability. We propose a global framework that can be applied to uncover molecular signatures for cell-type deconvolution in arbitrary tissues using bulk transcriptome data. We also present two new molecular signatures for deconvolution of up to 16 immune cell types using microarray or RNA-seq data. All steps of our analysis were implemented in annotated Python notebooks (https://github.com/paulfogel/A2SIGN). To perform NTF, we used the NMTF package, which can be downloaded using Python pip install. Supplementary data are available at Bioinformatics online.

The Spatial Heterogeneity in Newly Diagnosed Multiple Myeloma Patients - from Sub-Clonal Architecture to the Immune Microenvironment

The Spatial Heterogeneity in Newly Diagnosed Multiple Myeloma Patients - from Sub-Clonal Architecture to the Immune Microenvironment

Cell-cell communication through FGF4 generates and maintains robust proportions of differentiated cell types in embryonic stem cells.

ABSTRACTDuring embryonic development and tissue homeostasis, reproducible proportions of differentiated cell types are specified from populations of multipotent precursor cells. Molecular mechanisms that enable both robust cell-type proportioning despite variable initial conditions in the precursor cells, and the re-establishment of these proportions upon perturbations in a developing tissue remain to be characterized. Here, we report that the differentiation of robust proportions of epiblast-like and primitive endoderm-like cells in mouse embryonic stem cell cultures emerges at the population level through cell-cell communication via a short-range fibroblast growth factor 4 (FGF4) signal. We characterize the molecular and dynamical properties of the communication mechanism and show how it controls both robust cell-type proportioning from a wide range of experimentally controlled initial conditions, as well as the autonomous re-establishment of these proportions following the isolation of one cell type. The generation and maintenance of reproducible proportions of discrete cell types is a new function for FGF signaling that might operate in a range of developing tissues.

ImmuCellAI-mouse: a tool for comprehensive prediction of mouse immune cell abundance and immune microenvironment depiction.

Immune cells are important components of the immune system and are crucial for disease initiation, progression, prognosis and survival. Although several computational methods have been designed for predicting the abundance of immune cells, very few tools are applicable to mouse. Given that, mouse is the most widely used animal model in biomedical research, there is an urgent need to develop a precise algorithm for predicting mouse immune cells. We developed a tool named Immune Cell Abundance Identifier for mouse (ImmuCellAI-mouse), for estimating the abundance of 36 immune cell (sub)types from gene expression data in a hierarchical strategy of three layers. Reference expression profiles and robust marker gene sets of immune cell types were curated. The abundance of cells in three layers was predicted separately by calculating the ssGSEA enrichment score of the expression deviation profile per cell type. Benchmark results showed high accuracy of ImmuCellAI-mouse in predicting most immune cell types, with correlation coefficients between predicted value and real cell proportion of most cell types being larger than 0.8. We applied ImmuCellAI-mouse to a mouse breast tumor dataset and revealed the dynamic change of immune cell infiltration during treatment, which is consistent with the findings of the original study but with more details. We also constructed an online server for ImmuCellAI-mouse, on which users can upload expression matrices for analysis. ImmuCellAI-mouse will be a useful tool for studying the immune microenvironment, cancer immunology and immunotherapy in mouse models, providing an indispensable supplement for human disease studies. Software is available at http://bioinfo.life.hust.edu.cn/ImmuCellAI-mouse/. Supplementary data are available at Bioinformatics online.

P-065: CyTOF and single cell RNA sequencing reveal altered T cell phenotypes in Multiple Myeloma patients: implications for immunotherapy

<h3>Background</h3> Immune therapies have had a major impact in multiple myeloma (MM). Responses induced in relapsed and/or refractory MM (RRMM) patients (pts) are unprecedented; however the key challenge is their durability. Most immunotherapeutic approaches rely on the fitness of pts' T cells but exposure to multiple therapies may alter immune cell numbers and function. Here we analyzed and compared the transcriptome and phenotype of T cells from healthy donors (HD), newly diagnosed MM (NDMM) and RRMM pts. <h3>Methods</h3> Peripheral Blood Mononuclear Cells (PBMCs) from age-matched HD, NDMM and RRMM pts were collected before the start of a new line of treatment. Single cell RNA sequencing (scRNAseq) was performed and proportion of cell types were inferred using a reference PBMC dataset with Seurat. Cell type proportions were also estimated with CyTOF and conventional flow cytometry <h3>Results</h3> PBMCs from 10 HDs (median age 69, range 53-79), 20 NDMM pts (median age 71, range 54-88), 20 RRMM pts (median age 71, range 54-80) were analyzed. RRMM pts were exposed to 1 (n=8), 2 (n=6) or 3 (n=6) lines of treatment. All RRMM pts were exposed to a proteasome inhibitor, 75% were exposed to an immunomodulatory drug, and 65% received autologous stem cell transplantation. 48 out of 50 samples were processed by scRNAseq, 45 by CyTOF and 33 by flow cytometry. NDMM pts demonstrated a trend towards lower frequency of CD4 T cells compared to HD. This decreased number was accompanied by a decrease in the expression of genes involved in mitochondrial oxidative phosphorylation and an increase in expression of immediate early response genes (e.g. FOS, JUN, JUNB). In the CD8 compartment, CD8 naive cells were lower in NDMM compared to HD. No differences were found on CD8 central memory, effector memory and/or terminal effector populations In RRMM, the proportion of naive CD4 cells was significantly lower compared to NDMM (p < 0.05 by scRNAseq and CyTOF). This was confirmed by conventional flow cytometry (p < 0.05). No differences in CD4 central memory, effector memory and/or terminal effector proportions were observed. Decreases in the proportion of naive CD4 cells were observed as early as after 1 line of treatment and did not correlate with age, absolute lymphocyte count, previous exposure to lenalidomide or cyclophosphamide cumulative dose. Exposure to higher melphalan doses corelated with lower proportion of CD4 naive T cells. Compared with T cells from NDMM, T cells harvested from RRMM pts expressed higher levels of activation/exhaustion markers such as PD1 (p < 0.05) and HLA-DR (p < 0.05). <h3>Conclusion</h3> T cell phenotypes in MM pts are altered, especially after treatment. These findings may provide the rationale for the investigation of immunotherapy in NDMM and less heavily pretreated MM. Ongoing functional studies will demonstrate whether these phenotypic and transcriptomic changes lead to less fit T cells with consequent impact on effectiveness of T cell directed therapies.

A targeted solution for estimating the cell-type composition of bulk samples

BackgroundTo avoid false-positive findings and detect cell-type specific associations in methylation and transcription investigations with bulk samples, it is critical to know the proportions of the major cell-types.ResultsWe present a novel approach that allows for precise estimation of cell-type proportions using only a few highly informative methylation markers. The most reliable estimates were obtained with 17 amplicons (34 CpGs) using the MuSiC estimator, for which the average correlations between the estimated and the true cell-type proportions were 0.889. Furthermore, the estimates were not significantly different from the true values (P = 0.95) indicating that the estimator is unbiased and the standard deviation of the estimates further indicate high precision. Moreover, the overall variability of the estimates as measured by the Root Mean Squared Error (RMSE), which is a function of both bias and precision, was low (mean RMSE = 0.038). Taken together, these results indicate that the approach produced reliable estimates that are both unbiased and highly precise.ConclusionThis cost-effective approach for estimating cell-type proportions in bulk samples allows for enhanced targeted analysis, which in turn will minimize the risk of reporting false-positive findings and allowing for detection of cell-type specific associations. The approach is applicable across platforms and can be extended to assess cell-type proportions for various tissues.