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We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a sixfold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer. The interpretation of information-rich, high-throughput single-cell data is a challenge requiring sophisticated computational tools. Here the authors demonstrate a deep convolutional neural network that can classify cell cycle status on-the-fly.

Imaging flow cytometry combines the high-throughput capabilities of conventional flow cytometry with single-cell imaging. Here we demonstrate label-free prediction of DNA content and quantification of the mitotic cell cycle phases by applying supervised machine learning to morphological features extracted from brightfield and the typically ignored darkfield images of cells from an imaging flow cytometer. This method facilitates non-destructive monitoring of cells avoiding potentially confounding effects of fluorescent stains while maximizing available fluorescence channels. The method is effective in cell cycle analysis for mammalian cells, both fixed and live, and accurately assesses the impact of a cell cycle mitotic phase blocking agent. As the same method is effective in predicting the DNA content of fission yeast, it is likely to have a broad application to other cell types. Imaging flow cytometry enables high-throughput acquisition of fluorescence, brightfield and darkfield images of biological cells. Here, Blasi et al. demonstrate that applying machine learning algorithms on brightfield and darkfield images can detect cellular phenotypes without the need for fluorescent stains, enabling label-free assays.

Motivation: Accessing gene expression at the single cell level has unraveled often large heterogeneity among seemingly homogeneous cells, which remained obscured in traditional population based approaches. The computational analysis of single-cell transcriptomics data, however, still imposes unresolved challenges with respect to normalization, visualization and modeling the data. One such issue are differences in cell size, which introduce additional variability into the data, for which appropriate normalization techniques are needed. Otherwise, these differences in cell size may obscure genuine heterogeneities among cell populations and lead to overdispersed steady-state distributions of mRNA transcript numbers. Results: We present cgCorrect, a statistical framework to correct for differences in cell size that are due to cell growth in single-cell transcriptomics data. We derive the probability for the cell growth corrected mRNA transcript number given the measured, cell size dependent mRNA transcript number, based on the assumption that the average number of transcripts in a cell increases proportional to the cell's volume during cell cycle. cgCorrect can be used for both data normalization, and to analyze steady-state distributions used to infer the gene expression mechanism. We demonstrate its applicability on both simulated data and single-cell quantitative real-time PCR data from mouse blood stem and progenitor cells. We show that correcting for differences in cell size affects the interpretation of the data obtained by typically performed computational analysis.

We show that deep convolutional neural networks combined with non-linear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a 6-fold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer.

Infection is a main concern after haemopoietic stem cell transplantation (HSCT) and a major cause of transplant-related mortality. Some of these infections are preventable by vaccination. Most HSCT recipients lose their immunity to various pathogens as soon as the first months after transplant, irrespective of the pre-transplant donor or recipient vaccinations. Vaccination with inactivated vaccines is safe after transplantation and is an effective way to reinstate protection from various pathogens (eg, influenza virus and Streptococcus pneumoniae), especially for pathogens whose risk of infection is increased by the transplant procedure. The response to vaccines in patients with transplants is usually lower than that in healthy individuals of the same age during the first months or years after transplant, but it improves over time to become close to normal 2–3 years after the procedure. However, because immunogenic vaccines have been found to induce a response in a substantial proportion of the patients as early as 3 months after transplant, we recommend to start crucial vaccinations with inactivated vaccines from 3 months after transplant, irrespectively of whether the patient has or has not developed graft-versus-host disease (GvHD) or received immunosuppressants. Patients with GvHD have higher risk of infection and are likely to benefit from vaccination. Another challenge is to provide HSCT recipients the same level of vaccine protection as healthy individuals of the same age in a given country. The use of live attenuated vaccines should be limited to specific situations because of the risk of vaccine-induced disease.

Abstract Rationale Controversy still exists in the international literature regarding the need to use antimicrobials covering atypical pathogens when initially treating hospitalized patients with community-acquired pneumonia (CAP). In different regions of the world, monotherapy with a β-lactam antimicrobial is common. Objectives We sought to correlate the incidence of CAP due to atypical pathogens in different regions of the world with the proportion of patients treated with an atypical regimen in those same regions. In addition, we sought to compare clinical outcomes of patients with CAP treated with and without atypical coverage. Methods A secondary analysis was performed using two comprehensive international databases. World regions were defined as North America (I), Europe (II), Latin America (III), and Asia and Africa (IV). Time to reach clinical stability, length of hospital stay, and mortality were compared between patients treated with and without atypical coverage. Measurements and Main Results The incidence of CAP due to atypical pathogens from 4,337 patients was 22, 28, 21, and 20% in regions I–IV, respectively. The proportion of patients treated with atypical coverage from 2,208 patients was 91, 74, 53, and 10% in regions I–IV, respectively. Patients treated with atypical coverage had decreased time to clinical stability (3.7 vs. 3.2 d, p < 0.001), decreased length of stay (7.1 vs. 6.1 d, p < 0.01), decreased total mortality (11.1 vs. 7%, p < 0.01), and decreased CAP-related mortality (6.4 vs. 3.8%, p = 0.05). Conclusions The significant global presence of atypical pathogens and the better outcomes associated with antimicrobial regimens with atypical coverage support empiric therapy for all hospitalized patients with CAP with a regimen that covers atypical pathogens.

Axicabtagene ciloleucel (axi-cel) and tisagenlecleucel (tisa-cel) have both demonstrated impressive clinical activity in relapsed/refractory (R/R) diffuse large B cell lymphoma (DLBCL). In this study, we analyzed the outcome of 809 patients with R/R DLBCL after two or more previous lines of treatment who had a commercial chimeric antigen receptor (CAR) T cells order for axi-cel or tisa-cel and were registered in the retrospective French DESCAR-T registry study ( NCT04328298 ). After 1:1 propensity score matching ( n  = 418), the best overall response rate/complete response rate (ORR/CRR) was 80%/60% versus 66%/42% for patients treated with axi-cel compared to tisa-cel, respectively ( P  < 0.001 for both ORR and CRR comparisons). After a median follow-up of 11.7 months, the 1-year progression-free survival was 46.6% for axi-cel and 33.2% for tisa-cel (hazard ratio (HR) = 0.61; 95% confidence interval (CI), 0.46–0.79; P  = 0.0003). Overall survival (OS) was also significantly improved after axi-cel infusion compared to after tisa-cel infusion (1-year OS 63.5% versus 48.8%; HR = 0.63; 95% CI, 0.45–0.88; P  = 0.0072). Similar findings were observed using the inverse probability of treatment weighting statistical approach. Grade 1–2 cytokine release syndrome was significantly more frequent with axi-cel than with tisa-cel, but no significant difference was observed for grade ≥3. Regarding immune effector cell-associated neurotoxicity syndrome (ICANS), both grade 1–2 and grade ≥3 ICANS were significantly more frequent with axi-cel than with tisa-cel. In conclusion, our matched comparison study supports a higher efficacy and also a higher toxicity of axi-cel compared to tisa-cel in the third or more treatment line for R/R DLBCL. Analysis of outcomes of over 800 patients with relapsed/refractory diffuse large B cell lymphoma, treated with commercially available CAR T cell therapy, supports higher efficacy and also a higher toxicity of axicabtagene ciloleucel compared to tisagenlecleucel as the third or more treatment line for this type of tumor.

Patients with haematological malignancies are at high risk of infection because of various mechanisms of humoral and cell-mediated immune deficiencies, which mainly depend on underlying disease and specific therapies. Some of these infections are vaccine preventable. However, these malignancies are different from each other, and the treatment approaches are diverse and rapidly evolving, so it is difficult to have a common programme for vaccination in a haematology ward. Additionally, because of insufficient training about the topic, vaccination is an area often neglected by haematologists, and influenced by cultural differences, even among health-care workers, in compliance to vaccines. Several issues are encountered when addressing vaccination in haematology: the small size of the cohorts that makes it difficult to show the clinical benefits of vaccination, the subsequent need to rely on biological parameters, their clinical pertinence not being established in immunocompromised patients, scarcity of clarity on the optimal timing of vaccination in complex treatment schedules, and the scarcity of data on long-term protection in patients receiving treatments. Moreover, the risk of vaccine-induced disease with live-attenuated vaccines strongly limits their use. Here we summarise guidelines for patients without transplantations, and address the issue by the haematological group—myeloid and lymphoid—of diseases, with a special consideration for children with acute leukaemia.