Biology subjects

Saeys, Y.

Publications and source records attributed to Saeys, Y..

35 records · Page 2Linked to original sources

bioRxiv2022

Single-cell molecular profiling using ex vivo functional readouts fuels precision oncology in glioblastoma

BackgroundFunctional profiling of freshly isolated glioblastoma cells is being evaluated as a next-generation method for precision oncology. While promising, its success largely depends on the method to evaluate treatment activity which requires sufficient resolution and specificity. MethodsHere, we describe the precision oncology by single-cell profiling using ex vivo readouts of functionality (PROSPERO) assay to evaluate the intrinsic susceptibility of high- grade brain tumor cells to respond to therapy. Different from other assays, PROSPERO extends beyond life/death screening by rapidly evaluating acute molecular drug responses at single-cell resolution. ResultsThe PROSPERO assay was developed by correlating short-term single-cell molecular signatures using CyTOF to long-term cytotoxicity readouts in representative patient- derived glioblastoma cell cultures (n=14) that were exposed to radiotherapy and the small- molecule p53/MDM2 inhibitor AMG232. The predictive model was subsequently projected to evaluate drug activity in freshly resected GBM samples from patients (n=34). Here, PROSPERO revealed an overall limited capacity of tumor cells to respond to therapy, as reflected by the inability to induce key molecular markers upon ex vivo treatment exposure, while retaining proliferative capacity, insights that were validated in PDX models. This approach also allowed the investigation of cellular plasticity, which in PDCLs highlighted therapy-induced proneural-to-mesenchymal transitions, while in patients samples this was more heterogeneous. ConclusionPROSPERO provides a precise way to evaluate therapy efficacy by measuring molecular drug responses using specific biomarker changes in freshly resected brain tumor samples, in addition to providing key functional insights in cellular behavior, which may ultimately complement standard, clinical biomarker evaluations.

cancer biology↗

bioRxiv2022

Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability

Computational methods represent the lifeblood of modern molecular biology. Benchmarking is important for all methods, but with a focus here on computational methods, benchmarking is critical to dissect important steps of analysis pipelines, formally assess performance across common situations as well as edge cases, and ultimately guide users on what tools to use. Benchmarking can also be important for community building and advancing methods in a principled way. We conducted a meta-analysis of recent single-cell benchmarks to summarize the scope, extensibility, neutrality, as well as technical features and whether best practices in open data and reproducible research were followed. The results highlight that while benchmarks often make code available and are in principle reproducible, they remain difficult to extend, for example, as new methods and new ways to assess methods emerge. In addition, embracing containerization and workflow systems would enhance reusability of intermediate benchmarking results, thus also driving wider adoption.

bioinformatics↗

bioRxiv2022

CD8 memory precursor cells generation is a continuous process

In this work, we studied the generation of memory precursor cells following an acute infection by analyzing single-cell RNA-seq data that contained CD8 T cells collected during the postinfection expansion phase. We used different tools to reconstruct the developmental trajectory that CD8 T cells followed after activation. Cells that exhibited a memory precursor signature were identified and positioned on this trajectory. We found that memory precursors are generated continuously with increasing numbers being generated over time. Similarly, expression of genes associated with effector functions was also found to be raised in memory precursors at later time points. The ability of cells to enter quiescence to differentiate into memory cells was confirmed by BrdU pulse-chase experiment in vivo. Analysis of cell counts indicates that the vast majority of memory cells are generated at later time points from cells that have extensively divided.

immunology↗

bioRxiv2021

DALI (Diversity AnaLysis Interface): a novel tool for the integrated analysis of multimodal single cell RNAseq data and immune receptor profiling.

Single-cell RNA sequencing is instrumental to unravel the cellular and transcriptomic heterogeneity of T and B cells in health and disease. Recent technological advances add additional layers of information allowing researchers to simultaneously explore the transcriptomic, surface protein and immune receptor diversity during adaptive immune responses. The increasing data complexity poses a burden on the workload for bioinformaticians, who are often not familiar with the specificities and biology of immune receptor profiling. The wet-lab modalities and sequencing capabilities currently have outpaced bioinformatics solutions, which forms an ever-increasing barrier for many biologists to analyze their datasets. Here, we present DALI (Diversity AnaLysis Interface), a software package to identify and analyze T cell and B cell receptor diversity in high-throughput single-cell sequencing data. DALI aims to support bioinformaticians with a functional toolbox, allowing seamless integration of multimodal scRNAseq and immune receptor profiling data generated through 10X Genomics Cell Ranger software. The R-based package builds further on workflows using the Seurat package and other existing tools for BCR/TCR analyses. In addition, DALI is designed to engage immunologists having limited coding experience with their data, using a browser-based interactive graphical user interface. The implementation of DALI can effectively lead to a two-way communication between wet-lab scientists and bioinformaticians to advance the analysis of complex datasets.

bioinformatics↗

bioRxiv2021

Spatial proteogenomics reveals distinct and evolutionarily-conserved hepatic macrophage niches

The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here, we present a spatial proteogenomic atlas of the healthy human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and bile-duct macrophages. We also uncover the respective spatially-resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that bile-duct macrophages are induced by local lipid exposure, while Kupffer cells crucially depend on their crosstalk with hepatic stellate cells via the evolutionarily-conserved ALK1-BMP9/10 axis.

immunology↗

bioRxiv2021

Analysis of phloem trajectory links tissue maturation to cell specialization.

The mechanisms that allow cells in the plant meristem to coordinate tissue-wide maturation gradients with specialized cell networks are critical for indeterminate growth. Here, we reconstructed the protophloem developmental trajectory of 19 cells from cell birth to terminal differentiation at single cell resolution in the Arabidopsis root. We found that cellular specification is mediated near the stem cell niche by PHLOEM EARLY DOF (PEAR) transcription factors. However, the PEAR dependent differentiation program is repressed by a broad gradient of PLETHORA (PLT) transcription factors, which directly inhibit PEARs own direct target ALTERED PHLOEM DEVELOPMENT (APL). The dissipation of PLT gradient around 7 cells from the stem cell activates APL expression, and a subsequent transitional network that results in a "seesaw" pattern of mutual inhibition over developmental time. Together, we provide a mechanistic understanding of how morphogen-like maturation gradients interface with cell-type specific transcriptional regulators to stage cellular differentiation.

plant biology↗

bioRxiv2020

Comprehensive benchmarking of single cell RNA sequencing technologies for characterizing cellular perturbation

Technological advances in transcriptome sequencing of single cells continues to provide an unprecedented view on tissue composition and cellular heterogeneity. While several studies have compared different single cell RNA-seq methods with respect to data quality and their ability to distinguish cell subpopulations, none of these studies investigated the heterogeneity of the cellular transcriptional response upon a chemical perturbation. In this study, we evaluated the transcriptional response of NGP neuroblastoma cells upon nutlin-3 treatment using the C1, ddSeq and Chromium single cell systems. These devices and library preparation methods are representative for the wide variety of platforms, ranging from microfluid chips to droplet-based systems and from full transcript sequencing to 3-prime end sequencing. In parallel, we used bulk RNA-seq for molecular characterization of the transcriptional response. Two complementary metrics to evaluate performance were applied: the first is the number and identity of differentially expressed genes as defined in consensus by two statistical models, and the second is the enrichment analysis of biological signals. Where relevant, to make the data more comparable, we downsampled sequencing library size, selected cell subpopulations based on specific RNA abundance features, or created pseudobulk samples. While the C1 detects the highest number of genes per cell and better resembles bulk RNA-seq, the Chromium identifies most differentially expressed genes, albeit still substantially fewer than bulk RNA-seq. Gene set enrichment analyses reveals that detection of a limited set of the most abundant genes in single cell RNA-seq experiments is sufficient for molecular phenotyping. Finally, single cell RNA-seq reveals a heterogeneous response of NGP neuroblastoma cells upon nutlin-3 treatment, revealing putative late-responder or resistant cells, both undetected in bulk RNA-seq experiments.

molecular biology↗

bioRxiv2020

Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq

Multiplexing of samples in single-cell RNA-seq studies allows significant reduction of experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or - lipids allow barcoding sample-specific cells, a process called hashing. Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines. Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects.

genomics↗

bioRxiv2020

Deterministic scRNA-seq of individual intestinal organoids reveals new subtypes and coexisting distinct stem cell pools

Single-cell RNA-sequencing (scRNA-seq) has transformed our ability to resolve cellular properties across systems. However, current scRNA-seq platforms are one-size-fits-all approaches that are tailored toward large cell inputs (> 1,000 cells), rendering them inefficient and costly when processing small, individual tissue samples. This important drawback tends to be resolved by loading bulk samples, but this yields confounded mosaic cell population read-outs. To overcome these technological limitations, we developed a deterministic, mRNA-capture bead and cell co-encapsulation dropleting system, DisCo. We demonstrate that DisCo enables precise particle and cell positioning and droplet sorting control through combined machine-vision and multilayer microfluidics. In comparison to other microfluidics systems, the active flow control driving DisCo, enables continuous operation and processing of low-input samples (< 100 cells) at high capture efficiency (> 70%). To underscore the unique capabilities of our approach, we analyzed intestinal organoid development by "DisCo-ing" 31 individual organoids at varying developmental stages. This revealed extensive organoid heterogeneity, identifying distinct subtypes including a regenerative fetal-like Ly6a+ stem cell population which persists as symmetrical cysts even under differentiation conditions. Furthermore, we uncovered a so far uncharacterized "gobloid" subtype consisting predominantly of precursor and mature (Muc2+) goblet cells. These findings demonstrate the unique power of DisCo in providing high-resolution snapshots of cellular heterogeneity among small, individual tissues.

bioengineering↗

bioRxiv2020

Single cell profiling of immature human postnatal thymocytes resolves the complexity of intra-thymic lineage differentiation and thymus seeding precursors.

During postnatal life, thymopoiesis depends on the continuous colonization of the thymus by bone marrow derived hematopoietic progenitors that migrate through the bloodstream. In human, the nature of these thymus immigrants has remained unclear. Here, we employ single-cell RNA sequencing on approximately 70.000 CD34+ thymocytes to unravel the heterogeneity of the human immature postnatal thymocytes. Integration of bone marrow and peripheral blood precursors datasets identifies several putative thymus seeding precursors that display heterogeneity for currently used surface markers as revealed by CITEseq. Besides T cell precursors, we discover branches of intrathymic developing dendritic cells with predominantly plasmacytoid DCs. Trough trajectory inference, we delineate the transcriptional dynamics underlying early human T-lineage development from which we predict transcription factor modules that drive stage-specific steps of human T cell development. Thus, our work resolves the heterogeneity of thymus seeding precursors in human and reveals the molecular mechanisms that drive their in vivo cell fate.

immunology↗

bioRxiv2020

dyngen: a multi-modal simulator for spearheading new single-cell omics analyses

We present dyngen, a novel, multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution. dyngen is more flexible than current single-cell simulation engines, and allows better method development and benchmarking, thereby stimulating development and testing of novel computational methods. We demonstrate its potential for spearheading novel computational methods on three novel applications: aligning cell developmental trajectories, single-cell regulatory network inference and estimation of RNA velocity.

bioinformatics↗

bioRxiv2020

A cell atlas of human thymic development defines T cell repertoire formation

The thymus provides a nurturing environment for the differentiation and selection of T cells, a process orchestrated by their interaction with multiple thymic cell types. We utilised single-cell RNA-sequencing (scRNA-seq) to create a cell census of the human thymus and to reconstruct T-cell differentiation trajectories and T-cell receptor (TCR) recombination kinetics. Using this approach, we identified and located in situ novel CD8+ T-cell populations, thymic fibroblast subtypes and activated dendritic cell (aDC) states. In addition, we reveal a bias in TCR recombination and selection, which is attributed to genomic position and suggests later commitment of the CD8+ T-cell lineage. Taken together, our data provide a comprehensive atlas of the human thymus across the lifespan with new insights into human T-cell development.

immunology↗

bioRxiv2019

The RNA Atlas, a single nucleotide resolution map of the human transcriptome

The human transcriptome consists of various RNA biotypes including multiple types of non-coding RNAs (ncRNAs). Current ncRNA compendia remain incomplete partially because they are almost exclusively derived from the interrogation of small- and polyadenylated RNAs. Here, we present a more comprehensive atlas of the human transcriptome that is derived from matching polyA-, total-, and small-RNA profiles of a heterogeneous collection of nearly 300 human tissues and cell lines. We report on thousands of novel RNA species across all major RNA biotypes, including a hitherto poorly-cataloged class of non-polyadenylated single-exon long non-coding RNAs. In addition, we exploit intron abundance estimates from total RNA-sequencing to test and verify functional regulation by novel non-coding RNAs. Our study represents a substantial expansion of the current catalogue of human ncRNAs and their regulatory interactions. All data, analyses, and results are available in the R2 web portal and serve as a basis to further explore RNA biology and function.

genetics↗

bioRxiv2019

Classification of human white blood cells using machine learning for stain-free imaging flow cytometry

Imaging flow cytometry (IFC) produces up to 12 different information-rich images of single cells at a throughput of 5000 cells per second. Yet often, cell populations are still studied using manual gating, a technique that has several drawbacks. Firstly, it is hard to reproduce. Secondly, it is subjective and biased. And thirdly, it is time-consuming for large experiments. Therefore, it would be advantageous to replace manual gating with an automated process, which could be based on stain-free measurements originating from the brightfield and darkfield image channels. To realise this potential, advanced data analysis methods are required, in particular, machine learning. Previous works have successfully tested this approach on cell cycle phase classification with both a classical machine learning approach based on manually engineered features, and a deep learning approach. In this work, we compare both approaches extensively on the complex problem of white blood cell classification. Four human whole blood samples were assayed on an ImageStream-X MK II imaging flow cytometer. Two samples were stained for the identification of 8 white blood cell types, while two other sample sets were stained for the identification of resting and active eosinophils. For both datasets, four machine learning classifiers were evaluated on stain-free imagery using stratified 5-fold cross-validation. On the white blood cell dataset the best obtained results were 0.776 and 0.697 balanced accuracy for classical machine learning and deep learning, respectively. On the eosinophil dataset this was 0.866 and 0.867 balanced accuracy. From the experiments we conclude that classifying distinct cell types based on only stain-free images is possible with these techniques. However, both approaches did not always succeed in making reliable cell subtype classifications. Also, depending on the cell type, we find that even though the deep learning approach requires less expert input, it performs on par with a classical approach.

bioinformatics↗

bioRxiv2019

A ‘Human-in-the-Loop’ Approach for Semi-automated Image Restoration in Electron Microscopy

The recent advent of 3D in Electron Microscopy (EM) has allowed for detection of detailed sub-cellular nanometer resolution structures. While being a scientific breakthrough, this has also caused an explosion in dataset size, necessitating the development of automated workflows. Automated workflows typically benefit reproducibility and throughput compared to manual analysis. The risk of automation is that it ignores the expertise of the microscopy user that comes with manual analysis. To mitigate this risk, this paper presents a hybrid paradigm. We propose a human-in-the-loop (HITL) approach that combines expert microscopy knowledge with the power of large-scale parallel computing to improve EM image quality through advanced image restoration algorithms. An interactive graphical user interface, publicly available as an ImageJ plugin, was developed to allow biologists to use our framework in an intuitive and user-friendly fashion. We show that this plugin improves visualization of EM ultrastructure and subsequent (semi-)automated segmentation and image analysis.

bioinformatics↗

bioRxiv2019

Trajectory-based differential expression analysis for single-cell sequencing data

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression levels during biological processes such as the cell cycle, cell type differentiation, and cellular activation. Downstream of trajectory inference, it is vital to discover genes that are associated with the lineages in the trajectory to illuminate the underlying biological processes. Furthermore, genes that are differentially expressed between developmental/activational lineages might be highly relevant to further unravel the system under study. Current data analysis procedures, however, typically cluster cells and assess differential expression between the clusters, which fails to exploit the continuous resolution provided by trajectory inference to its full potential. The few available non-cluster-based methods only assess broad differences in gene expression between lineages, hence failing to pinpoint the exact types of divergence. We introduce a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of (i) within-lineage differential expression by detecting associations between gene expression and pseudotime over an entire lineage or by comparing gene expression between points/regions within the lineage and (ii) between-lineage differential expression by comparing gene expression between lineages over the entire lineages or at specific points/regions. By incorporating observation-level weights, the model additionally allows to account for zero inflation, commonly observed in single-cell RNA-seq data from full-length protocols. We evaluate the method on simulated and real datasets from droplet-based and full-length protocols, and show that the flexible inference framework is capable of yielding biological insights through a clear interpretation of the data.

bioinformatics↗

bioRxiv2018

A comparison of single-cell trajectory inference methods: towards more accurate and robust tools

Using single-cell-omics data, it is now possible to computationally order cells along trajectories, allowing the unbiased study of cellular dynamic processes. Since 2014, more than 50 trajectory inference methods have been developed, each with its own set of methodological characteristics. As a result, choosing a method to infer trajectories is often challenging, since a comprehensive assessment of the performance and robustness of each method is still lacking. In order to facilitate the comparison of the results of these methods to each other and to a gold standard, we developed a global framework to benchmark trajectory inference tools. Using this framework, we compared the trajectories from a total of 29 trajectory inference methods, on a large collection of real and synthetic datasets. We evaluate methods using several metrics, including accuracy of the inferred ordering, correctness of the network topology, code quality and user friendliness. We found that some methods, including Slingshot, TSCAN and Monocle DDRTree, clearly outperform other methods, although their performance depended on the type of trajectory present in the data. Based on our benchmarking results, we therefore developed a set of guidelines for method users. However, our analysis also indicated that there is still a lot of room for improvement, especially for methods detecting complex trajectory topologies. Our evaluation pipeline can therefore be used to spearhead the development of new scalable and more accurate methods, and is available at github.com/dynverse/dynverse.\n\nTo our knowledge, this is the first comprehensive assessment of trajectory inference methods. For now, we exclusively evaluated the methods on their default parameters, but plan to add a detailed parameter tuning procedure in the future. We gladly welcome any discussion and feedback on key decisions made as part of this study, including the metrics used in the benchmark, the quality control checklist, and the implementation of the method wrappers. These discussions can be held at github.com/dynverse/dynverse/issues.

bioinformatics↗