Reliable isolation of human mesenchymal stromal cells from bone marrow biopsy specimens in patients after allogeneic hematopoietic cell transplantation

Reduced proliferation of bone marrow MSC after allogeneic stem cell transplantation is associated with clinical outcome

Engraftment and differentiation of donor hematopoietic stem cells is decisive for the clinical success of allogeneic stem cell transplantation (alloSCT) and depends on the recipient's bone marrow (BM) niche. A damaged niche contributes to poor graft function after alloSCT; however, the underlying mechanisms and the role of BM multipotent mesenchymal stromal cells (MSC) are ill-defined. Upon multivariate analysis in 732 individuals, we observed a reduced presence of proliferation-capable MSC in BM aspirates from patients (N = 196) who had undergone alloSCT. This was confirmed by paired analysis in 30 patients showing a higher frequency of samples with a lack of MSC presence post-alloSCT compared with pre-alloSCT. This reduced MSC presence was associated with reduced survival of patients after alloSCT and specifically with impaired graft function. Post-alloSCT MSC showed diminished in vitro proliferation along with a transcriptional antiproliferative signature, upregulation of epithelial-mesenchymal transition and extracellular matrix pathways, and altered impact on cytokine release upon contact with hematopoietic cells. To avoid in vitro culture bias, we isolated the CD146+/CD45-/HLA-DR- BM cell fraction, which comprised the entire MSC population. The post-alloSCT isolated native CD146+MSC showed a similar reduction in proliferation capacity and shared the same antiproliferative transcriptomic signature as for post-alloSCT colony-forming unit fibroblast-derived MSC. Taken together, our data show that alloSCT confers damage to the proliferative capacity of native MSC, which is associated with reduced patient survival after alloSCT and impaired engraftment of allogeneic hematopoiesis. These data represent the basis to elucidate mechanisms of BM niche reconstitution after alloSCT and its therapeutic manipulation.

Perturbations of mesenchymal stromal cells after allogeneic hematopoietic cell transplantation predispose for bone marrow graft-versus-host-disease

Functional impairment of the bone marrow (BM) niche has been suggested as a major reason for prolonged cytopenia and secondary graft failure after allogeneic hematopoietic cell transplantation (alloHCT). Because mesenchymal stromal cells (MSCs) serve as multipotent progenitors for several niche components in the BM, they might play a key role in this process. We used collagenase digested trephine biopsies to directly quantify MSCs in 73 patients before (n = 18) and/or after alloHCT (n = 65). For the first time, we demonstrate that acute graft-versus-host disease (aGvHD, n = 39) is associated with a significant decrease in MSC numbers. MSC reduction can be observed even before the clinical onset of aGvHD (n = 10). Assessing MSCs instantly after biopsy collection revealed phenotypic and functional differences depending on the occurrence of aGvHD. These differences vanished during ex vivo expansion. The MSC endotypes observed revealed an enhanced population of donor-derived classical dendritic cells type 1 and alloreactive T cells as the causing agent for compartmental inflammation and MSC damage before clinical onset of aGvHD was ascertained. In conclusion, MSCs endotypes may constitute a predisposing conductor of alloreactivity after alloHCT preceding the clinical diagnosis of aGvHD.

AIDeveloper: Deep Learning Image Classification in Life Science and Beyond

Artificial intelligence (AI)-based image analysis has increased drastically in recent years. However, all applications use individual solutions, highly specialized for a particular task. Here, an easy-to-use, adaptable, and open source software, called AIDeveloper (AID) to train neural nets (NN) for image classification without the need for programming is presented. AID provides a variety of NN-architectures, allowing to apply trained models on new data, obtain performance metrics, and export final models to different formats. AID is benchmarked on large image datasets (CIFAR-10 and Fashion-MNIST). Furthermore, models are trained to distinguish areas of differentiated stem cells in images of cell culture. A conventional blood cell count and a blood count obtained using an NN are compared, trained on >1.2 million images, and demonstrated how AID can be used for label-free classification of B- and T-cells. All models are generated by non-programmers on generic computers, allowing for an interdisciplinary use.