Biology subjects

Gustafsson, J.

Publications and source records attributed to Gustafsson, J..

6 recordsLinked to original sources

bioRxiv2023

kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq

The term "RNA-seq" refers to a collection of assays based on sequencing experiments that involve quantifying RNA species from bulk tissue, from single cells, or from single nuclei. The kallisto, bustools, and kb-python programs are free, open-source software tools for performing this analysis that together can produce gene expression quantification from raw sequencing reads. The quantifications can be individualized for multiple cells, multiple samples, or both. Additionally, these tools allow gene expression values to be classified as originating from nascent RNA species or mature RNA species, making this workflow amenable to both cell-based and nucleus-based assays. This protocol describes in detail how to use kallisto and bustools in conjunction with a wrapper, kb-python, to preprocess RNA-seq data.

bioinformatics↗

bioRxiv2023

Multiomic single-cell analysis identifies von Willebrand factor and TIM3-expressing BCR-ABL1+ CML stem cells

Tyrosine kinase inhibitors (TKI) only rarely eradicate leukemic stem cells (LSC) in chronic myeloid leukemia (CML) which commonly necessitates life-long therapy and monitoring of patients. Understanding details of leukemic hematopoiesis in CML may identify targetable pathways for sustained LSC elimination. This study utilized multiomic single-cell characterization of the CD14-CD34+ hematopoietic stem and progenitor cell (HSPC) compartment in CML. Combined proteo-transcriptomic profiling of 597 genes and 51 proteins (CITE-seq) was performed along with parallel detection of BCR-ABL1 transcripts in 70,000 HSPC from 16 chronic phase patients and five healthy controls. CD14-CD34+ HSPC from diagnosis samples displayed distinct myeloid cell bias with cells mainly annotated as LSC, lympho-myeloid progenitors (LMP)-II, erythrocyte and megakaryocyte progenitors, while few hematopoietic stem cells (HSC), LMP-I, dendritic cell or B cell progenitors were detected. In-depth analysis of the immature CD14-CD34+CD38-/low compartment revealed two distinct populations of BCR-ABL1-expressing CML LSC (denoted LSC-I and LSC-II), where LSC-I showed features of quiescence and CD45RA-cKIT-CD26+ TKI therapy-resistant phenotype. These subtypes of immature LSC showed high surface expression of TIM3 and transcription of the von Willebrand factor gene (VWF). Our findings imply that expression of VWF and TIM3 distinguish LSC from HSC and may be linked to aberrant myeloid-biased hematopoiesis in CML. Additionally, the results identify TIM3 as a conceivable target for sustained elimination of immature LSC in CML. Key pointsO_LIWe present a method to detect BCR-ABL1 expression at the single-cell level that is compatible with high-throughput CITE-seq C_LIO_LIThe most immature BCR-ABL1-expressing LSC population in primary CML shows enhanced expression of von Willebrand factor and TIM3 C_LI

cancer biology↗

bioRxiv2022

Brain energy metabolism is optimized to minimize the cost of enzyme synthesis and transport

Energy metabolism of the brain is poorly understood partly due to the complex morphology of neurons. Here we used metabolic models that estimate costs of enzyme usage per pathway, enzyme utilization over time, and enzyme transport to evaluate a paradigm that suggests that brain energy metabolism is optimized to minimize enzyme synthesis and transportation costs. Our models recapitulate known metabolic behaviors and provide explanation for the astrocyte-neuron lactate shuttle theory.

systems biology↗

bioRxiv2022

Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data

Single-cell RNA sequencing has the potential to unravel the differences in metabolism across cell types and cell states in both the healthy and diseased human body. The use of existing knowledge in the form of genome-scale metabolic models (GEMs) holds promise to strengthen such analyses, but the combined use of these two methods requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the tINIT algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile, emphasizing the need to study them separately rather than to build models from bulk RNA-Seq data. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.

systems biology↗

bioRxiv2022

Cellular limitation of enzymatic capacity explains glutamine addiction in cancers

The tumor microenvironment is comprised of a complex mixture of different cell types interacting under conditions of nutrient deprivation, but the metabolism therein is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell types in vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common hypothesis that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified over 200 potential metabolites that could support collaboration between cancer cells and cancer associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme constrained models for accurate prediction of metabolism in cells and tumor microenvironments.

cancer biology↗

bioRxiv2020

Addressing the pooled amplification paradox with unique molecular identifiers in single-cell RNA-seq

The incorporation of unique molecular identifiers (UMIs) in single-cell RNA-seq assays allows for the removal of amplification bias in the estimation of gene abundances. We show that UMIs can also be used to address a problem resulting from incomplete sequencing of amplified molecules in sequencing libraries that can lead to bias in gene abundance estimates. Our method, called BUTTERFLY, is based on a zero truncated negative binomial estimator and is implemented in the kallisto bustools single-cell RNA-seq workflow. We demonstrate its efficacy using a range of datasets and show that it can invert the relative abundance of certain genes in cases of a pooled amplification paradox.

bioinformatics↗