Biology subjects

Schaafsma, E.

Publications and source records attributed to Schaafsma, E..

6 recordsLinked to original sources

bioRxiv2026

Long-Term Human Skin Platform for Modeling Chronic Inflammation, Environmental Stress, and Therapeutic Intervention

Chronic inflammation drives tissue dysfunction and aging, yet the dynamic interplay between persistent inflammatory signaling and structural deterioration remains difficult to study in human-relevant systems. Here, an advanced long-term human skin platform is presented that preserves native tissue architecture and epidermal, stromal, and immune-associated molecular programs for up to 4 weeks. Using this system, sustained cytokine-driven inflammation was modeled, demonstrating chronic inflammatory transcriptional programs, progressive histopathological changes, and persistent inflammatory mediator secretion that were broadly suppressed by the JAK inhibitor tofacitinib. Using aged donor tissue, prolonged senolytic-associated treatment attenuated inflammatory and remodeling pathways. Finally, UVB exposure triggered coordinated stress and inflammatory responses that were partially mitigated using topical sunscreen, demonstrating compatibility with environmental stress modeling and topical intervention within preserved tissue architecture. Together, these findings establish a versatile human skin platform for modeling chronic inflammation, aging-associated tissue remodeling, and environmental stress, providing a translational framework for investigating skin tissue dysfunction and evaluating therapeutic interventions.

bioengineering↗

bioRxiv2024

Heterogeneity and plasticity of the naive CD4+ T cell compartment

While naive CD4+ T cells have historically been considered a homogenous population, recent studies have provided evidence that functional heterogeneity exists within this population. Using single cell RNA sequencing (scRNAseq), we identify five transcriptionally distinct naive CD4+ T cell subsets that emerge within the single positive stage in the thymus: a quiescence cluster (TQ), a memory-like cluster (TMEM), a TCR reactive cluster (TTCR), an IFN responsive cluster (TIFN), and an undifferentiated cluster (TUND). Elevated expression of transcription factors KLF2, Mx1, and Nur77 within the TQ, TIFN, and TMEM clusters, respectively, allowed enrichment of these subsets for further analyses. Functional studies using sorted cells revealed that naive T cell subsets have distinctive functional biases upon stimulation. Furthermore, treatment of mice with inflammatory stimuli imparted a state of reduced responsiveness on naive T cells, evidenced by a reduction in cytokine production ex vivo. In human lupus patients, naive CD4+ T cell cluster frequencies were distorted, with the TIFN cluster expanding proportionately with disease score. Our data show that naive T cells are influenced by host environment, with functional consequences manifesting upon activation. These findings highlight a need to explore how naive T cells can become distorted in cancer, autoimmunity, and infectious diseases. SummaryThis study describes the transcriptional heterogeneity of murine and human naive CD4+ T cells as comprising of multiple discrete clusters that impact CD4+ T cell fate and trajectories. Naive CD4+ T cells experiencing inflammatory environments exhibit an altered transcriptional state that influences their functional trajectory.

immunology↗

bioRxiv2022

TimiGP: inferring inter-cell functional interactions and clinical values in the tumor immune microenvironment through gene pairs

Determining how immune cells functionally interact in the tumor microenvironment and identifying their biological roles and clinical values are critical for understanding cancer progression and developing new therapeutic strategies. Here we introduce TimiGP, a computational method to infer inter-cell functional interaction networks and annotate the corresponding prognostic effect from bulk gene expression and survival statistics data. When applied to metastatic melanoma, TimiGP overcomes the prognostic bias caused by immune co-infiltration and identifies the prognostic value of immune cells consistent with their anti- or pro-tumor roles. It reveals the functional interaction network in which the interaction X[->]Y indicates a more positive impact of cell X than Y on survival. This network provides immunological insights to facilitate the development of prognostic models, as evidenced by our computational-friendly, biologically interpretable, independently validated models. By leveraging single-cell RNA-seq data for specific immune cell subsets, TimiGP has the flexibility to delineate the tumor microenvironment at different resolutions and is readily applicable to a wide range of cancer types.

bioinformatics↗

bioRxiv2021

Tumor cell intrinsic and extrinsic features predicts prognosis in estrogen receptor positive breast cancer

Although estrogen-receptor-positive (ER+) breast cancer is generally associated with favorable prognosis, clinical outcome varies substantially among patients. Genomic assays have been developed and applied to predict patient prognosis for personalized treatment. We hypothesize that the recurrence risk of ER+ breast cancer patients is determined by both genomic mutations intrinsic to tumor cells and extrinsic immunological features in the tumor microenvironment. Based on the Cancer Genome Atlas (TCGA) breast cancer data, we identified the 72 most common genomic aberrations (including gene mutations and indels) in ER+ breast cancer and defined sample-specific scores that systematically characterized the deregulated pathways intrinsic to tumor cells. To further consider tumor cell extrinsic features, we calculated immune infiltration scores for six major immune cell types. Many individual intrinsic features are predictive of patient prognosis in ER+ breast cancer, and some of them achieved comparable accuracy with the Oncotype DX assay. In addition, statistical learning models that integrated these features predicts the recurrence risk of patients with significantly better performance than the Oncotype DX assay. As a proof-of-concept, our study indicates the great potential of genomic and immunological features in prognostic prediction for improving breast cancer precision medicine. The framework introduced in this work can be readily applied to other cancers. Author SummaryMany genomic biomarker tests such as Oncotype DX have been developed for breast cancer and have helped guide clinical decisions. We have developed gene signatures to integrate cancer genomic and transcriptomic data to characterize the downstream effect of driver genomic events. These signatures recapitulate the de-regulated pathways underlying the corresponding driver genomic events and are more correlated with clinical phenotypes such as recurrence free survival than mutation status alone. We apply this framework to ER+ breast cancer and define gene signatures for a total of 72 most commonly observed genomic events including gene mutations, amplifications and deletions. We find that many of these gene signatures are predictive of patient prognosis in ER+ breast cancer, and some of them achieved comparable accuracy with the Oncotype DX assay. We combine these tumor-intrinsic signatures with infiltration signatures for major immune cell types (tumor-extrinsic features) to construct integrative models for prognosis prediction. The models predicts the recurrence risk of patients with significantly better performance than the Oncotype DX assay.

genomics↗

bioRxiv2021

A de novo paradigm for male infertility

IntroductionDe novo mutations (DNMs) are known to play a prominent role in sporadic disorders with reduced fitness1. We hypothesize that DNMs play an important role in male infertility and explain a significant fraction of the genetic causes of this understudied disorder. To test this hypothesis, we performed trio-based exome-sequencing in a unique cohort of 185 infertile males and their unaffected parents. Following a systematic analysis, 29 of 145 rare protein altering DNMs were classified as possibly causative of the male infertility phenotype. We observed a significant enrichment of Loss-of-Function (LoF) DNMs in LoF-intolerant genes (p-value=1.00x10-5) as well as predicted pathogenic missense DNMs in missense-intolerant genes (p-value=5.01x10-4). One DNM gene identified, RBM5, is an essential regulator of male germ cell pre-mRNA splicing2. In a follow-up study, 5 rare pathogenic missense mutations affecting this gene were observed in a cohort of 2,279 infertile patients, with no such mutations found in a cohort of 5,784 fertile men (p-value=0.009). Our results provide the first evidence for the role of DNMs in severe male infertility and point to many new candidate genes affecting fertility.

genetics↗

bioRxiv2018

Pan-cancer systematic identification of lncRNAs associated with cancer prognosis

The \"dark matter\" of the genome harbors several non-coding RNA species including IncRNAs, which have been implicated in neoplasias but remain understudied. RNA-seq has provided deep insights into the nature of lncRNAs in cancer but current RNA-seq data are rarely accompanied by longitudinal patient survival information. In contrast, a plethora of microarray studies have collected these clinical metadata that can be leveraged to identify novel associations between gene expression and clinical phenotypes. In this study, we developed an analysis framework that computationally integrates RNA-seq and microarray data to systematically screen 9,463 lncRNAs for association with mortality risk across 20 cancer types. In total, we identified a comprehensive list of associations between lncRNAs and patient survival and demonstrate that these prognostic lncRNAs are under selective pressure and may be functional. Our results provide valuable insights that facilitate further exploration of lncRNAs and their potential as cancer biomarkers and drug targets.

cancer biology↗