The journey of neutropoiesis: how complex landscapes in bone marrow guide continuous neutrophil lineage determination

Neutrophils are the most abundant white blood cell, and differentiate in homeostasis in the bone marrow from hematopoietic stem cells (HSCs) via multiple intermediate progenitor cells into mature cells that enter the circulation. Recent findings support a continuous model of differentiation in the bone marrow of heterogeneous HSCs and progenitor populations. Cell fate decisions both at the level of proliferation and differentiation are enforced through expression of lineage-determining transcription factors (LDTFs) and their interactions, that are influenced by both intrinsic (intracellular) as well as extrinsic (extracellular) mechanisms. Neutrophil homeostasis is subjected to positive feedback loops, stemming from the gut microbiome, as well as negative feedback loops resulting from the clearance of apoptotic neutrophils by mature macrophages. Finally, the cellular kinetics regarding the replenishing of the mature neutrophil pool is discussed in light of recent, contradictory data.

Flow cytometry and pharmacokinetics

Multiparametric flow cytometry is a powerful cellular analysis tool used in various stages of drug development. In adoptive cell therapies, the flow cytometry methods are used for the evaluation of advanced cellular products during manufacturing and to monitor cellular kinetics after infusion. In this report, we discussed the bioanalytical method development challenges to monitor cellular kinetics in CAR-T cell therapies. These method development challenges include procuring positive control samples for the development of the method, flow cytometry panel design, LLOQ, prestain sample stability, staining reagents and data analysis.

Iron, Ferritin and Hepcidin Levels in Patients with Iron Deficiency Anemia

This study was conducted to identify the role of some blood parameters, levels of some hormones, proteins, and cellular kinetics that have a role in iron transport and storage, in addition to their relationship with each other and with blood and sex parameters for patients with severe and moderate iron deficiency anemia, and to compare the parameters with healthy people. The study period lasted for the period from (December 2020 until March 2021) The presence of iron deficiency anemia was confirmed by examining the blood picture and the criteria of hepcidin and ferritin. The results showed a significant decrease in the in the concentration of hepcidin, ferritin and iron in patients compared to the control group.

Protein structural features predict responsiveness to pharmacological chaperone treatment for three lysosomal storage disorders

Three-dimensional structures of proteins can provide important clues into the efficacy of personalized treatment. We perform a structural analysis of variants within three inherited lysosomal storage disorders, comparing variants responsive to pharmacological chaperone treatment to those unresponsive to such treatment. We find that predicted ΔΔG of mutation is higher on average for variants unresponsive to treatment, in the case of datasets for both Fabry disease and Pompe disease, in line with previous findings. Using both a single decision tree and an advanced machine learning approach based on the larger Fabry dataset, we correctly predict responsiveness of three Gaucher disease variants, and we provide predictions for untested variants. Many variants are predicted to be responsive to treatment, suggesting that drug-based treatments may be effective for a number of variants in Gaucher disease. In our analysis, we observe dependence on a topological feature reporting on contact arrangements which is likely connected to the order of folding of protein residues, and we provide a potential justification for this observation based on steady-state cellular kinetics.

Protein structural features predict responsiveness to pharmacological chaperone treatment for three lysosomal storage disorders

Three-dimensional structures of proteins can provide important clues into the efficacy of personalized treatment. We perform a structural analysis of variants within three inherited lysosomal storage disorders, comparing variants responsive to pharmacological chaperone treatment to those unresponsive to such treatment. We find that predicted ∆∆G of mutation is higher on average for variants unresponsive to treatment, in the case of datasets for both Fabry disease and Pompe disease, in line with previous findings. Using both a single decision tree and an advanced machine learning approach based on the larger Fabry dataset, we correctly predict responsiveness of three Gaucher disease variants, and we provide predictions for untested variants. Many variants are predicted to be responsive to treatment, suggesting that drug-based treatments may be effective for a number of variants in Gaucher disease. In our analysis, we observe dependence on a topological feature reporting on contact arrangements which is likely connected to the order of folding of protein residues, and we provide a potential justification for this observation based on steady-state cellular kinetics.

Optimisation of the Synthesis and Cell Labelling Conditions for [89Zr]Zr-oxine and [89Zr]Zr-DFO-NCS: a Direct In Vitro Comparison in Cell Types with Distinct Therapeutic Applications

Background There is a need to better characterise cell-based therapies in preclinical models to help facilitate their translation to humans. Long-term high-resolution tracking of the cells in vivo is often impossible due to unreliable methods. Radiolabelling of cells has the advantage of being able to reveal cellular kinetics in vivo over time. This study aimed to optimise the synthesis of the radiotracers [89Zr]Zr-oxine (8-hydroxyquinoline) and [89Zr]Zr-DFO-NCS (p-SCN-Bn-Deferoxamine) and to perform a direct comparison of the cell labelling efficiency using these radiotracers. Procedures Several parameters, such as buffers, pH, labelling time and temperature, were investigated to optimise the synthesis of [89Zr]Zr-oxine and [89Zr]Zr-DFO-NCS in order to reach a radiochemical conversion (RCC) of >95 % without purification. Radio-instant thin-layer chromatography (iTLC) and radio high-performance liquid chromatography (radio-HPLC) were used to determine the RCC. Cells were labelled with [89Zr]Zr-oxine or [89Zr]Zr-DFO-NCS. The cellular retention of 89Zr and the labelling impact was determined by analysing the cellular functions, such as viability, proliferation, phagocytotic ability and phenotypic immunostaining. Results The optimised synthesis of [89Zr]Zr-oxine and [89Zr]Zr-DFO-NCS resulted in straightforward protocols not requiring additional purification. [89Zr]Zr-oxine and [89Zr]Zr-DFO-NCS were synthesised with an average RCC of 98.4 % (n = 16) and 98.0 % (n = 13), respectively. Cell labelling efficiencies were 63.9 % (n = 35) and 70.2 % (n = 30), respectively. 89Zr labelling neither significantly affected the cell viability (cell viability loss was in the range of 1–8 % compared to its corresponding non-labelled cells, P value > 0.05) nor the cells’ proliferation rate. The phenotype of human decidual stromal cells (hDSC) and phagocytic function of rat bone-marrow-derived macrophages (rMac) was somewhat affected by radiolabelling. Conclusions Our study demonstrates that [89Zr]Zr-oxine and [89Zr]Zr-DFO-NCS are equally effective in cell labelling. However, [89Zr]Zr-oxine was superior to [89Zr]Zr-DFO-NCS with regard to long-term stability, cellular retention, minimal variation between cell types and cell labelling efficiency.

Graph-based machine learning reveals rules of spatiotemporal cell interactions in tissues

Extracting the rules of cell-to-cell interactions in tissue dynamics is challenging even if high-resolution live microscopy is accessible. In order to seek and compare the different rules enforced in developing and homeostatic tissues, it will be desirable to have a systematic method that outputs the rules of multi-cellular kinetics simply from live images and cell tracks. Here we demonstrate that graph neural network (GNN)-based models can predict cell fate in the mammalian epidermis when spatiotemporal graph constructed from cell tracks and contacts are given as inputs. By extracting the rules learned by GNN, we find neighbor cell fate inductions and inhibitions consistent with previous findings as well as some that have been previously overlooked. This study demonstrates how GNN-based methods can be useful in inferring stochastic dynamics such as multi-cellular kinetics involving proliferation and loss of agents.