Biology subjects

Stumpf, P. S.

Publications and source records attributed to Stumpf, P. S..

8 recordsLinked to original sources

bioRxiv2021

Dual Dean entrainment with volume ratio modulation for efficient co-encapsulation: Extreme single-cell indexing

The future of single cell diversity screens involves ever-larger sample sizes, dictating the need for higher throughput methods with low analytical noise to accurately describe the nature of the cellular system. Current approaches are limited by the Poisson statistic, requiring dilute cell suspensions and associated losses in throughput. In this contribution, we apply Dean entrainment to both cell and bead inputs, defining different volume packets to effect efficient co-encapsulation. Volume ratio scaling was explored to identify optimal conditions. This enabled the co-encapsulation of single cells with reporter beads at rates of ~1 million cells/hour, while increasing assay signal-to-noise with cell multiplet rates of ~2.5% and capturing ~70% of cells. The method, called Pirouette-seq, extends our capacity to investigate biological systems. TOC AbstractPirouette-seq involves cell and reporter bead inertial ordering for efficient co-encapsulation, achieving a throughput of 1 million cells/hour, a 2.5% multiplet rate and a 70% cell capture efficiency.

cell biology↗

bioRxiv2020

Heterogeneity and 'memory in stem cell populations

Modern single cell experiments have revealed unexpected heterogeneity in apparently functionally pure cell populations. However, we are still lacking a conceptual framework to understand this heterogeneity. Here, we propose that cellular memories - the ability of individual cells to record their developmental past and adapt their response to their environment accordingly - are an essential ingredient in any such theory. We illustrate this idea by considering a simple age-structured model of stem cell proliferation. Using this model we argue that heterogeneity is central to stem cell population function, and memories naturally explain why stem cell numbers increase through life, yet regenerative potency simultaneously declines.

cell biology↗

bioRxiv2020

Single cell RNA sequence analysis of human bone marrow samples reveals new targets for isolation of skeletal stem cells using DNA-coated gold nanoparticles

There is a wealth of data indicating human bone marrow derived stromal cells (HBMSCs) contain the skeletal stem cell (SSC) with the potential to differentiate along the stromal osteogenic, adipogenic and chondrogenic lineages. However, despite these advances, current methods to isolate skeletal stem cells (SSCs) from human tissues have proved challenging as no single specific marker has been identified limiting understanding of SSC fate, immunophenotype and the widespread clinical application of these cells. While a number of cell surface markers can enrich for SSCs, none of the proposed markers, alone, provide a platform to isolate single cells with the ability to form bone, cartilage, and adipose tissue in humans. The current study details the application of oligonucleotide-coated nanoparticles, spherical nucleic acids (SNAs), to rapidly isolate human cells using mRNAs signatures detected in SSCs in real time, to identify stem and progenitor skeletal populations using single cell RNA sequencing. Based on scRNA-seq of samples from 11 patients, this method was able to identify novel targets for SSC enrichment, which were assessed in a total of 80 patients. This methodology was able to isolate potential SSCs found at a frequency of <1 in 1,000,000 in human bone marrow, with a capacity for tri-lineage differentiation in vitro. The current approach provides new targets and a platform to advance SSC isolation, enrichment with significant therapeutic impact therein.

cell biology↗

bioRxiv2019

Mapping biology from mouse to man using transfer learning

Biomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt from these experiments will transfer to the human. Yet, it is commonly the case that biology does not transfer effectively, often for unknown reasons. Despite its importance to translational research this transfer process is not currently rigorously quantified. Here, we show that transfer learning - the branch of machine learning that concerns passing information from one domain to another - can be used to efficiently map biology from mouse to man, using the bone marrow (BM) as a representative example of a complex tissue. We first trained an artificial neural network (ANN) to accurately recognize various different cell types in mouse BM using data obtained from single-cell RNA-sequencing (scRNA-Seq) experiments. We found that this ANN, trained exclusively on mouse data, was able to identify individual human cells obtained from comparable scRNA-Seq experiments of human BM with 83% overall accuracy. However, while some human cell types were easily identified, others were not, indicating important differences in biology. To obtain a more accurate map of the human BM we then retrained the mouse ANN using scRNA-Seq data from a limited sample of human BM cells. Typically, less than 10 human cells of a given type were needed to accurately learn its representation in the updated model. In some cases, human cell identities could be inferred directly from the mouse ANN without retraining, via a process of biologically-guided zero-shot learning. These results show how machine learning can be used to reconstruct complex biology from limited data and have broad implications for biomedical research.

systems biology↗

bioRxiv2019

Genomic programming of antigen cross-presentation in IRF4-expressing human Langerhans cells

Langerhans cells (LCs) in the epidermis present MHC I and MHC II-restricted antigens thereby priming either CD8 or CD4 T cell immune responses. The genomic programs and transcription factors regulating antigen presentation in LCs remain to be elucidated. We show human LCs are highly efficient in MHC I-antigen cross-presentation but lack the transcription factor IRF8 that is critical in dendritic cells. LC migration from the epidermis enhances their ability to cross-present antigens and is accompanied by the induction of the transcription factor IRF4, whose expression is correlated by scRNA-seq with genes involved in ubiquitin-dependent protein degradation. Chromatin profiling reveals enrichment of EICE and AICE composite DNA binding motifs in regulatory regions of antigen-presentation genes, which can be recognized by IRF4 in conjunction with PU.1 or BATF3 expressed in LCs. Thus, the genomic programming of human LCs including inducible expression of IRF4 with enhanced cross-presentation distinguishes them from conventional dendritic cells.

immunology↗

bioRxiv2018

Cell division history determines hematopoietic stem cell potency

Loss of stem cell self-renewal may underpin aging. Here, we combined single cell profiling, deep-learning, mathematical modelling and in vivo functional studies to explore how hematopoietic stem cell (HSC) division patterns evolve with age. We trained an artificial neural network (ANN) to accurately identify cell types in the hematopoietic hierarchy and predict their age from their gene expression patterns. We then used this ANN to compare daughter cell identities immediately after HSC divisions, and found that HSC self-renewal declines sharply with age. Furthermore, while HSC cell divisions are deterministic and intrinsically-regulated in young and old age, they become stochastic and niche-sensitive in mid-life. Analysis of evolving division patterns indicated that the self-renewal ability an individual HSC depends upon number times it has divided previously. We propose a model of HSC proliferation that accurately predicts the accumulation of HSCs with age, and reconciles the stochastic and instructive views of fate commitment.

cell biology↗

bioRxiv2017

Pattern analysis of pluripotency reveals the molecular basis of naïve, primed and formative states

The molecular regulatory network underlying stem cell pluripotency has been intensively studied, and we now have a reliable ensemble model for the average pluripotent cell. However, evidence of significant cell-to-cell variability suggests that the activity of this network varies within individual stem cells, leading to differential processing of environmental signals and variability in cell fates. Here, we adapt a method originally designed for face recognition to infer regulatory network patterns within individual cells from single-cell expression data. Using this method we identify three distinct network configurations in cultured mouse embryonic stem cells - corresponding to naive and formative pluripotent states and an early primitive endoderm state - and associate these configurations with particular combinations of regulatory network activity archetypes that govern different aspects of the cells response to environmental stimuli, cell cycle status and core information processing circuitry. These results show how variability in cell identities arise naturally from alterations in underlying regulatory network dynamics and demonstrate how methods from machine learning may be used to better understand single cell biology, and the collective dynamics of cell communities.

systems biology↗

bioRxiv2017

Stem cell differentiation is a stochastic process with memory

Pluripotent stem cells are able to self-renew indefinitely in culture and differentiate into all somatic cell types in vivo. While much is known about the molecular basis of pluripotency, the molecular mechanisms of lineage commitment are complex and only partially understood. Here, using a combination of single cell profiling and mathematical modeling, we examine the differentiation dynamics of individual mouse embryonic stem cells (ESCs) as they progress from the ground state of pluripotency along the neuronal lineage. In accordance with previous reports we find that cells do not transit directly from the pluripotent state to the neuronal state, but rather first stochastically permeate an intermediate primed pluripotent state, similar to that found in the maturing epiblast in development. However, analysis of rate at which individual cells enter and exit this intermediate metastable state using a hidden Markov model reveals that the observed ESC and epiblast-like macrostates conceal a chain of unobserved cellular microstates, which individual cells transit through stochastically in sequence. These hidden microstates ensure that individual cells spend well-defined periods of time in each functional macrostate and encode a simple form of epigenetic memory that allows individual cells to record their position on the differentiation trajectory. To examine the generality of this model we also consider the differentiation of mouse hematopoietic stem cells along the myeloid lineage and observe remarkably similar dynamics, suggesting a general underlying process. Based upon these results we suggest a statistical mechanics view of cellular identities that distinguishes between functionally-distinct macrostates and the many functionally-similar molecular microstates associated with each macrostate. Taken together these results indicate that differentiation is a discrete stochastic process amenable to analysis using the tools of statistical mechanics.

systems biology↗