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Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer cells and an heterogeneous stroma. In recent years, automated microscopy technologies are allowing the study of live cells over extended periods of time, simplifying the task of compiling large image databases. For instance, there have been several studies oriented towards building machine learning systems capable of automatically classifying images of different cell types (i.e. motor neurons, stem cells). In this work we were interested in classifying breast cancer cells as live or dead, based on a set of automatically retrieved morphological characteristics using image processing techniques. Our hypothesis is that live-dead classification can be performed without any staining and using only bright-field images as input. We tackled this problem using the JIMT-1 breast cancer cell line that grows as an adherent monolayer. First, a vast image set composed by JIMT-1 human breast cancer cells that had been exposed to a chemotherapeutic drug treatment (doxorubicin and paclitaxel) or vehicle control was compiled. Next, several classifiers were trained based on well-known convolutional neural networks (CNN) backbones to perform supervised classification using labels obtained from fluorescence microscopy images associated with each bright-field image. Model performances were evaluated and compared on a large number of bright-field images. The best model reached an AUC = 0.941 for classifying breast cancer cells without treatment. Furthermore, it reached AUC = 0.978 when classifying breast cancer cells under drug treatment. Our results highlight the potential of machine learning and computational image analysis to build new diagnosis tools that benefit the biomedical field by reducing cost, time, and stimulating work reproducibility. More importantly, we analyzed the way our classifiers clusterize bright-field images in the learned high-dimensional embedding and linked these groups to salient visual characteristics in live-dead cell biology observed by trained experts.

Although neutrophils have crucial functions in microbial killing, they can also trigger tissue damage via the release of reactive oxygen species and proinflammatory mediators. Robert Zeiser and colleagues now report that neutrophils also contribute to the severity of graft-versus-host disease following translocation of bacteria in the gut induced by the conditioning regimen for allogeneic hematopoietic cell transplantation. Acute graft-versus-host disease (GVHD) considerably limits wider usage of allogeneic hematopoietic cell transplantation (allo-HCT). Antigen-presenting cells and T cells are populations customarily associated with GVHD pathogenesis. Of note, neutrophils are the largest human white blood cell population. The cells cleave chemokines and produce reactive oxygen species, thereby promoting T cell activation 1 , 2 . Therefore, during an allogeneic immune response, neutrophils could amplify tissue damage caused by conditioning regimens. We analyzed neutrophil infiltration of the mouse ileum after allo-HCT by in vivo myeloperoxidase imaging and found that infiltration levels were dependent on the local microbial flora and were not detectable under germ-free conditions. Physical or genetic depletion of neutrophils reduced GVHD-related mortality. The contribution of neutrophils to GVHD severity required reactive oxygen species (ROS) because selective Cybb (encoding cytochrome b-245, beta polypeptide, also known as NOX2) deficiency in neutrophils impairing ROS production led to lower levels of tissue damage, GVHD-related mortality and effector phenotype T cells. Enhanced survival of Bcl-xL transgenic neutrophils increased GVHD severity. In contrast, when we transferred neutrophils lacking Toll-like receptor-2 (TLR2), TLR3, TLR4, TLR7 and TLR9, which are normally less strongly activated by translocating bacteria, into wild-type C57BL/6 mice, GVHD severity was reduced. In humans, severity of intestinal GVHD strongly correlated with levels of neutrophils present in GVHD lesions. This study describes a new potential role for neutrophils in the pathogenesis of GVHD in both mice and humans.

T cells play a pivotal role in cancer research, particularly in immunotherapy, which harnesses the immune system to target malignancies. However, conventional expansion methods face limitations such as high reagent consumption, contamination risks, and difficulties in maintaining suspension cells in dynamic culture environments. This study presents a microfluidic system for long-term culture of non-adherent cells, featuring automated perfusion and image acquisition. The system integrates deep learning-based image analysis, which quantifies cell coverage and estimates cell numbers, and efficiently processes large volumes of data. The performance of this deep learning approach was benchmarked against the widely used Trainable Weka Segmentation (TWS) plugin for Fiji. Additionally, two distinct lab-on-a-chip (LOC) devices were evaluated independently: the commercial ibidi® LOC and a custom-made PDMS LOC. The setup supported the proliferation of Jurkat cells and primary human T cells without significant loss during medium exchange. Each platform proved suitable for long-term expansion while offering distinct advantages in terms of design, cell seeding and recovery, and reusability. This integrated approach enables extended experiments with minimal manual intervention, stable perfusion, and supports multi-reagent administration, offering a powerful platform for advancing suspension cell research in immunotherapy.

Treatment of cancer patients with autologous T-cells expressing a chimeric antigen receptor (CAR) is one of the most promising therapeutic modalities for hematological malignancy treatment. For this treatment, primary T-cell expansion is needed. Microfluidic technologies can be used to better understand T-cell activation and proliferation. Microfluidics have had a meaningful impact in the way experimental biology and biomedical research are approached in general. Furthermore, microfluidic technology allows the generation of large amounts of data and enables the use of image processing for analysis. However, one of the major technical hurdles involved in growing suspension cells under microfluidic conditions is their immobilization, to avoid washing them out of the microfluidic chip during medium renewal. In this work, we use a multilevel microfluidic chip to successfully capture and immobilize suspension cells. Jurkat cells and T-cells are isolated through traps to microscopically track their development and proliferation after activation over a period of 8 days. The T-cell area of four independent microchannels was compared and there is no statistically significant difference between them (ANOVA p-value = 0.976). These multilevel microfluidic chips provide a new method of studying T-cell activation.

Low-dose metronomic (LDM) chemotherapy is an alternative to conventional chemotherapy and is the most frequently used approach in low dose chemotherapy regimens. The selection of patients, drug dosages, and dosing intervals in LDM is empirical. In this study, we systematically examined the schedule-dependent interaction of drugs on a breast cancer cell line (BCC) cultured in chambered coverslips. The LDM studies were combined with cell staining in order to better characterize different cell states and cell death modes, including caspase-dependent apoptosis, caspase-independent cell death and autophagy-dependent cell death. Microscope images were examined using the Fiji Trainable Weka Segmentation plugin to analyse cell area in 7500 images showing different modes of cell death. Paclitaxel combined with LDM chemotherapy demonstrated a reduction in the area covered by live cells. In contrast, there was an induction of high levels of cell death due to caspase-dependent apoptosis.

A model for hematopoiesis is presented that explicitly includes the erythrocyte, granulocyte, and thrombocyte lineages and their common precursors. A small number of stem cells proliferate and differentiate through different compartments to produce the vast number of blood cells needed every day. Growth factors regulate the proliferation of cells dependent on the current demand. We provide a steady state analysis of the model and rough parameter estimates. Furthermore, we extend the model to include mutations that alter the replicative capacity of cells and introduce differentiation blocks. With these mutations the model develops signs of acute myeloid leukemia.

Low-dose metronomic (LDM) chemotherapy is an alternative to conventional chemotherapy and is the most frequently used approach in low dose chemotherapy regimens. The selection of patients, drug dosages, and dosing intervals in LDM is empirical. In this study, we systematically examined the schedule-dependent interaction of drugs on a breast cancer cell line (BCC) cultured in chambered coverslips. The LDM studies were combined with cell staining in order to better characterize different cell states and cell death modes, including caspase-dependent apoptosis, caspase-independent cell death and autophagy-dependent cell death. Microscope images were examined using the Fiji Trainable Weka Segmentation plugin to analyse cell area in 7500 images showing different modes of cell death. Paclitaxel combined with LDM chemotherapy demonstrated a reduction in the area covered by live cells. In contrast, there was an induction of high levels of cell death due to caspase-dependent apoptosis.

Interaction of cancer cells with their microenvironment generated by stromal cells is essential for tumor cell survival and influences the localization of tumor growth. Here we demonstrate that hedgehog ligands secreted by bone-marrow, nodal and splenic stromal cells function as survival factors for malignant lymphoma and plasmacytoma cells derived from transgenic Eμ- Myc mice or isolated from humans with these malignancies. Hedgehog pathway inhibition in lymphomas induced apoptosis through downregulation of Bcl2, but was independent of p53 or Bmi1 expression. Blockage of hedgehog signaling in vivo inhibited expansion of mouse lymphoma cells in a syngeneic mouse model and reduced tumor mass in mice with fully developed disease. Our data indicate that stromally induced hedgehog signaling may provide an important survival signal for B- and plasma-cell malignancies in vitro and in vivo . Disruption of this interaction by hedgehog pathway inhibition could provide a new strategy in lymphoma and multiple myeloma therapy.

The age of antibiotics, which began with the discovery of penicillin in 1929, marks an important period in medical history, as deadly bacterial infections seemed to be a thing of the past. However, the tide turned as more and more resistance to all existing antibiotics emerged, leading to the still ongoing era of antibiotic resistance. To counter this development and slow it down, it is important to administer the appropriate, effective antibiotics, which requires the clinical transition from empirical to targeted antibiotic therapy. Antimicrobial susceptibility testing (AST) is a key pillar in the implementation of targeted antibiotic therapy. This review provides an overview of the different phenotypic and genotypic AST methods with their advantages and disadvantages, focusing on the turnaround time from patient to result, including preculture. In addition, an outlook on their future potential is given, also considering the impact of artificial intelligence on this field. This article provides an overview of recent advances in antimicrobial susceptibility testing using phenotypic and genotypic approaches. The advantages and disadvantages of the respective approaches as well as their future potential including the implementation of artificial intelligence are outlined.