Lactation-associated macrophages exist in murine mammary tissue and human milk

Macrophages are involved in immune defense, organogenesis and tissue homeostasis. Macrophages contribute to the different phases of mammary gland remodeling during development, pregnancy and involution postlactation. Less is known about the dynamics of mammary gland macrophages in the lactation stage. Here, we describe a macrophage population present during lactation in mice. By multiparameter flow cytometry and single-cell RNA sequencing, we identified a lactation-induced CD11c⁺CX3CR1⁺Dectin-1⁺ macrophage population (liMac) that was distinct from the two resident F4/80^hi and F4/80^lo macrophage subsets present pregestationally. LiMacs were predominantly monocyte-derived and expanded by proliferation in situ concomitant with nursing. LiMacs developed independently of IL-34, but required CSF-1 signaling and were partly microbiota-dependent. Locally, they resided adjacent to the basal cells of the alveoli and extravasated into the milk. We found several macrophage subsets in human milk that resembled liMacs. Collectively, these findings reveal the emergence of unique macrophages in the mammary gland and milk during lactation.

Distinct immunological signatures discriminate severe COVID-19 from non-SARS-CoV-2-driven critical pneumonia

Immune profiling of COVID-19 patients has identified numerous alterations in both innate and adaptive immunity. However, whether those changes are specific to SARS-CoV-2 or driven by a general inflammatory response shared across severely ill pneumonia patients remains unknown. Here, we compared the immune profile of severe COVID-19 with non-SARS-CoV-2 pneumonia ICU patients using longitudinal, high-dimensional single-cell spectral cytometry and algorithm-guided analysis. COVID-19 and non-SARS-CoV-2 pneumonia both showed increased emergency myelopoiesis and displayed features of adaptive immune paralysis. However, pathological immune signatures suggestive of T cell exhaustion were exclusive to COVID-19. The integration of single-cell profiling with a predicted binding capacity of SARS-CoV-2 peptides to the patients' HLA profile further linked the COVID-19 immunopathology to impaired virus recognition. Toward clinical translation, circulating NKT cell frequency was identified as a predictive biomarker for patient outcome. Our comparative immune map serves to delineate treatment strategies to interfere with the immunopathologic cascade exclusive to severe COVID-19.

Single-cell profiling of myasthenia gravis identifies a pathogenic T cell signature

Myasthenia gravis (MG) is an autoimmune disease characterized by impaired neuromuscular signaling due to autoantibodies targeting the acetylcholine receptor. Although its auto-antigens and effector mechanisms are well defined, the cellular and molecular drivers underpinning MG remain elusive. Here, we employed high-dimensional single-cell mass and spectral cytometry of blood and thymus samples from MG patients in combination with supervised and unsupervised machine-learning tools to gain insight into the immune dysregulation underlying MG. By creating a comprehensive immune map, we identified two dysregulated subsets of inflammatory circulating memory T helper (Th) cells. These signature Th_CD103 and Th_GM cells populated the diseased thymus, were reduced in the blood of MG patients, and were inversely correlated with disease severity. Both signature Th subsets rebounded in the blood of MG patients after surgical thymus removal, indicative of their role as cellular markers of disease activity. Together, this in-depth analysis of the immune landscape of MG provides valuable insight into disease pathogenesis, suggests novel biomarkers and identifies new potential therapeutic targets for treatment.

Myasthenia gravis: From single cell signatures to cancer diagnosis

Myasthenia gravis is a rare but archetypic autoimmune disease that is characterized by the autoantibody-mediated disruption of the neuromuscular junction leading to a skeletal muscle weakness. Immunomodulatory treatment options for Myasthenia gravis patients are largely unspecific, include suppression of the entire immune compartment and are often accompanied by severe side effects.

In order to identify novel biomarkers for more targeted and effective therapeutic approaches, we combined high-dimensional mass and flow cytometry with supervised and unsupervised machine-learning algorithms. Analysis of the peripheral immune compartment of Myasthenia gravis patients and healthy controls revealed a cellular immune signature consisting of inflammatory memory T helper cells with a defined cytokine profile. The abundance of the identified leukocytes in the blood strongly correlated with the patients clinical disease activity, far better than auto-Ab titers. Moreover, we were able to locate T cells with the defined signature enriched in the inflamed thymus of Myasthenia gravis patients – the key organ for the induction and maintenance of the autoimmune disease. Lastly, using an unbiased pattern recognition approach, we identified lymphomas in a subset of Myasthenia gravis patients, further highlighting the potential of the applied analysis tools.