Publications by authors named "L D'Alessandro Gandolfo"

Tissue-resident memory T (T) cells are integral to tissue immunity, persisting in diverse anatomical sites where they adhere to a common transcriptional framework. How these cells integrate distinct local cues to adopt the common T cell fate remains poorly understood. Here, we show that whereas skin T cells strictly require transforming growth factor β (TGF-β) for tissue residency, those in other locations utilize the metabolite retinoic acid (RA) to drive an alternative differentiation pathway, directing a TGF-β-independent tissue residency program in the liver and synergizing with TGF-β to drive T cells in the small intestine.

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Skin-resident CD8 T cells include distinct interferon-γ-producing [tissue-resident memory T type 1 (T1)] and interleukin-17 (IL-17)-producing (T17) subsets that differentially contribute to immune responses. However, whether these populations use common mechanisms to establish tissue residence is unknown. In this work, we show that T1 and T17 cells navigate divergent trajectories to acquire tissue residency in the skin.

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Introduction: Diabetes mellitus in underrepresented racial and ethnic groups (URG) is rapidly increasing in incidence and has worse outcomes than diabetes in non-Hispanic White individuals. Rare and Atypical Diabetes Network (RADIANT) established recruitment targets based on the racial and ethnic distribution of the USA to enroll a diverse study population. We examined participation of URG across RADIANT study stages and described strategies to enhance recruitment and retention of URG.

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CD8 tumor-infiltrating lymphocytes with a tissue-resident memory T (T) cell phenotype are associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, the relative contribution of CD8 T cells to anti-tumor immunity and immune checkpoint blockade efficacy in breast cancer remains unknown. Here, we show that intratumoral CD8 T cells in murine mammary tumors transcriptionally resemble those from TNBC patients.

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Accurate identification and effective removal of unwanted variation is essential to derive meaningful biological results from RNA sequencing (RNA-seq) data, especially when the data come from large and complex studies. Using RNA-seq data from The Cancer Genome Atlas (TCGA), we examined several sources of unwanted variation and demonstrate here how these can significantly compromise various downstream analyses, including cancer subtype identification, association between gene expression and survival outcomes and gene co-expression analysis. We propose a strategy, called pseudo-replicates of pseudo-samples (PRPS), for deploying our recently developed normalization method, called removing unwanted variation III (RUV-III), to remove the variation caused by library size, tumor purity and batch effects in TCGA RNA-seq data.

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