Ex vivo-expanded human CD19+TIM-1+ regulatory B cells suppress immune responses in vivo and are dependent upon the TIM-1/STAT3 axis.

Regulatory B cells (Breg) are a heterogenous population with immune-modulating functions. The rarity of human IL-10+ Breg makes translational studies difficult. Here we report ex vivo expansion of human B cells with in vivo regulatory function (expBreg). CD154-stimulation of human CD19+ B cells drives >900-fold expansion of IL-10+ B cells that is maintained in culture for 14 days. Whilst expBreg-mediated suppressive function is partially dependent on IL-10 expression, CRISPR-mediated gene deletions demonstrate predominant roles for TIM-1 and CD154. TIM-1 regulates STAT3 signalling and modulates downstream suppressive function. In a clinically relevant humanised mouse model of skin transplantation, expBreg prolongs human allograft survival. Meanwhile, CD19+CD73-CD25+CD71+TIM-1+CD154+ Breg cells are enriched in the peripheral blood of human donors with cutaneous squamous cell carcinoma (SCC). TIM-1+ and pSTAT3+ B cells are also identified in B cell clusters within histological sections of human cutaneous SCC tumours. Our findings thus provide insights on Breg homoeostasis and present possible targets for Breg-related therapies.

Building digital twins of the human immune system: toward a roadmap.

Digital twins, customized simulation models pioneered in industry, are beginning to be deployed in medicine and healthcare, with some major successes, for instance in cardiovascular diagnostics and in insulin pump control. Personalized computational models are also assisting in applications ranging from drug development to treatment optimization. More advanced medical digital twins will be essential to making precision medicine a reality. Because the immune system plays an important role in such a wide range of diseases and health conditions, from fighting pathogens to autoimmune disorders, digital twins of the immune system will have an especially high impact. However, their development presents major challenges, stemming from the inherent complexity of the immune system and the difficulty of measuring many aspects of a patient's immune state in vivo. This perspective outlines a roadmap for meeting these challenges and building a prototype of an immune digital twin. It is structured as a four-stage process that proceeds from a specification of a concrete use case to model constructions, personalization, and continued improvement.

Nonlinear modelling of cancer: bridging the gap between cells and tumours.

Despite major scientific, medical and technological advances over the last few decades, a cure for cancer remains elusive. The disease initiation is complex, and including initiation and avascular growth, onset of hypoxia and acidosis due to accumulation of cells beyond normal physiological conditions, inducement of angiogenesis from the surrounding vasculature, tumour vascularization and further growth, and invasion of surrounding tissue and metastasis. Although the focus historically has been to study these events through experimental and clinical observations, mathematical modelling and simulation that enable analysis at multiple time and spatial scales have also complemented these efforts. Here, we provide an overview of this multiscale modelling focusing on the growth phase of tumours and bypassing the initial stage of tumourigenesis. While we briefly review discrete modelling, our focus is on the continuum approach. We limit the scope further by considering models of tumour progression that do not distinguish tumour cells by their age. We also do not consider immune system interactions nor do we describe models of therapy. We do discuss hybrid-modelling frameworks, where the tumour tissue is modelled using both discrete (cell-scale) and continuum (tumour-scale) elements, thus connecting the micrometre to the centimetre tumour scale. We review recent examples that incorporate experimental data into model parameters. We show that recent mathematical modelling predicts that transport limitations of cell nutrients, oxygen and growth factors may result in cell death that leads to morphological instability, providing a mechanism for invasion via tumour fingering and fragmentation. These conditions induce selection pressure for cell survivability, and may lead to additional genetic mutations. Mathematical modelling further shows that parameters that control the tumour mass shape also control its ability to invade. Thus, tumour morphology may serve as a predictor of invasiveness and treatment prognosis.