Objective assessment of stored blood quality by deep learning.

Stored red blood cells (RBCs) are needed for life-saving blood transfusions, but they undergo continuous degradation. RBC storage lesions are often assessed by microscopic examination or biochemical and biophysical assays, which are complex, time-consuming, and destructive to fragile cells. Here we demonstrate the use of label-free imaging flow cytometry and deep learning to characterize RBC lesions. Using brightfield images, a trained neural network achieved 76.7% agreement with experts in classifying seven clinically relevant RBC morphologies associated with storage lesions, comparable to 82.5% agreement between different experts. Given that human observation and classification may not optimally discern RBC quality, we went further and eliminated subjective human annotation in the training step by training a weakly supervised neural network using only storage duration times. The feature space extracted by this network revealed a chronological progression of morphological changes that better predicted blood quality, as measured by physiological hemolytic assay readouts, than the conventional expert-assessed morphology classification system. With further training and clinical testing across multiple sites, protocols, and instruments, deep learning and label-free imaging flow cytometry might be used to routinely and objectively assess RBC storage lesions. This would automate a complex protocol, minimize laboratory sample handling and preparation, and reduce the impact of procedural errors and discrepancies between facilities and blood donors. The chronology-based machine-learning approach may also improve upon humans' assessment of morphological changes in other biomedically important progressions, such as differentiation and metastasis.

Hematopoietic stem-cell senescence and myocardial repair - Coronary artery disease genotype/phenotype analysis of post-MI myocardial regeneration response induced by CABG/CD133+ bone marrow hematopoietic stem cell treatment in RCT PERFECT Phase 3.

BACKGROUND: Bone marrow stem cell clonal dysfunction by somatic mutation is suspected to affect post-infarction myocardial regeneration after coronary bypass surgery (CABG). METHODS: Transcriptome and variant expression analysis was studied in the phase 3 PERFECT trial post myocardial infarction CABG and CD133+ bone marrow derived hematopoetic stem cells showing difference in left ventricular ejection fraction (∆LVEF) myocardial regeneration Responders (n=14; ∆LVEF +16% day 180/0) and Non-responders (n=9; ∆LVEF -1.1% day 180/0). Subsequently, the findings have been validated in an independent patient cohort (n=14) as well as in two preclinical mouse models investigating SH2B3/LNK antisense or knockout deficient conditions. FINDINGS: 1. Clinical: R differed from NR in a total of 161 genes in differential expression (n=23, q<0•05) and 872 genes in coexpression analysis (n=23, q<0•05). Machine Learning clustering analysis revealed distinct RvsNR preoperative gene-expression signatures in peripheral blood acorrelated to SH2B3 (p<0.05). Mutation analysis revealed increased specific variants in RvsNR. (R: 48 genes; NR: 224 genes). 2. Preclinical:SH2B3/LNK-silenced hematopoietic stem cell (HSC) clones displayed significant overgrowth of myeloid and immune cells in bone marrow, peripheral blood, and tissue at day 160 after competitive bone-marrow transplantation into mice. SH2B3/LNK-/- mice demonstrated enhanced cardiac repair through augmenting the kinetics of bone marrow-derived endothelial progenitor cells, increased capillary density in ischemic myocardium, and reduced left ventricular fibrosis with preserved cardiac function. 3. VALIDATION: Evaluation analysis in 14 additional patients revealed 85% RvsNR (12/14 patients) prediction accuracy for the identified biomarker signature. INTERPRETATION: Myocardial repair is affected by HSC gene response and somatic mutation. Machine Learning can be utilized to identify and predict pathological HSC response. FUNDING: German Ministry of Research and Education (BMBF): Reference and Translation Center for Cardiac Stem Cell Therapy - FKZ0312138A and FKZ031L0106C, German Ministry of Research and Education (BMBF): Collaborative research center - DFG:SFB738 and Center of Excellence - DFG:EC-REBIRTH), European Social Fonds: ESF/IV-WM-B34-0011/08, ESF/IV-WM-B34-0030/10, and Miltenyi Biotec GmbH, Bergisch-Gladbach, Germany. Japanese Ministry of Health : Health and Labour Sciences Research Grant (H14-trans-001, H17-trans-002) TRIAL REGISTRATION: ClinicalTrials.gov NCT00950274.

Label-Free Leukemia Monitoring by Computer Vision.

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. While there are a number of well-recognized prognostic biomarkers at diagnosis, the most powerful independent prognostic factor is the response of the leukemia to induction chemotherapy (Campana and Pui: Blood 129 (2017) 1913-1918). Given the potential for machine learning to improve precision medicine, we tested its capacity to monitor disease in children undergoing ALL treatment. Diagnostic and on-treatment bone marrow samples were labeled with an ALL-discriminating antibody combination and analyzed by imaging flow cytometry. Ignoring the fluorescent markers and using only features extracted from bright-field and dark-field cell images, a deep learning model was able to identify ALL cells at an accuracy of >88%. This antibody-free, single cell method is cheap, quick, and could be adapted to a simple, laser-free cytometer to allow automated, point-of-care testing to detect slow early responders. Adaptation to other types of leukemia is feasible, which would revolutionize residual disease monitoring. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Prevalence of antibodies against Hepatitis D virus (HDV) in blood donors in Northern Germany.

OBJECTIVE: To assess the prevalence of HDV infections in German blood donors. METHOD: 167 donors with acute/chronic or resolved HBV infection and detectable antibodies against Hepatitis B core antigen (anti-HBc) were tested for antibodies against HDV (anti-HDV) by competitive ELISA. Samples with detectable anti-HDV or with HBsAg and/or HBV DNA were additionally investigated for HDV RNA. RESULTS: In nine (5.4 %) of the 167 donors, also HBsAg and HBV DNA were detectable. Anti-HDV was detectable in two of the 167 donors (1.2 %), additional four donors (2.4 %) had a borderline result. All of these donors tested negative for HBsAg and HBV DNA. Neither in samples with anti-HDV nor in HBsAg-/HBV DNA-positive samples, HDV RNA was detectable. CONCLUSIONS: At least 1.2 % of anti-HBc-positive blood donors have had an HDV infection. Although there is some evidence for a somewhat higher prevalence of HDV, the overall prevalence of HDV in Northern Germany is low.

Label-Free Identification of White Blood Cells Using Machine Learning.

White blood cell (WBC) differential counting is an established clinical routine to assess patient immune system status. Fluorescent markers and a flow cytometer are required for the current state-of-the-art method for determining WBC differential counts. However, this process requires several sample preparation steps and may adversely disturb the cells. We present a novel label-free approach using an imaging flow cytometer and machine learning algorithms, where live, unstained WBCs were classified. It achieved an average F1-score of 97% and two subtypes of WBCs, B and T lymphocytes, were distinguished from each other with an average F1-score of 78%, a task previously considered impossible for unlabeled samples. We provide an open-source workflow to carry out the procedure. We validated the WBC analysis with unstained samples from 85 donors. The presented method enables robust and highly accurate identification of WBCs, minimizing the disturbance to the cells and leaving marker channels free to answer other biological questions. It also opens the door to employing machine learning for liquid biopsy, here, using the rich information in cell morphology for a wide range of diagnostics of primary blood. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

A systematic survey of centrality measures for protein-protein interaction networks.

BACKGROUND: Numerous centrality measures have been introduced to identify "central" nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures. RESULTS: We used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network's topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities. CONCLUSIONS: The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.

Parvovirus B19: What Is the Relevance in Transfusion Medicine?

Parvovirus B19 (B19V) has been discovered in 1975. The association with a disease was unclear in the first time after the discovery of B19V, but meanwhile, the usually droplet transmitted B19V is known as the infectious agent of the "fifth disease," a rather harmless children's illness. But B19V infects erythrocyte progenitor cells and thus, acute B19V infection in patients with a high erythrocyte turnover may lead to a life-threatening aplastic crisis, and acutely infected pregnant women can transmit B19V to their unborn child, resulting in a hydrops fetalis and fetal death. However, in many adults, B19V infection goes unnoticed and thus many blood donors donate blood despite the infection. The B19V infection does not impair the blood cell counts in healthy blood donors, but after the acute infection with extremely high DNA concentrations exceeding 1010 IU B19V DNA/ml plasma is resolved, B19V DNA persists in the plasma of blood donors at low levels for several years. That way, many consecutive donations that contain B19V DNA can be taken from a single donor, but the majority of blood products from donors with detectable B19V DNA seem not to be infectious for the recipients from several reasons: first, many recipients had undergone a B19V infection in the past and have formed protective antibodies. Second, B19V DNA concentration in the blood product is often too low to infect the recipient. Third, after the acute infection, the presence of B19V DNA in the donor is accompanied by presumably neutralizing antibodies which are protective also for the recipient of his blood products. Thus, transfusion-transmitted (TT-) B19V infections are very rarely reported. Moreover, in most blood donors, B19V DNA concentration is below 1,000 IU/ml plasma, and no TT-B19V infections have been found by such low-viremic donations. Cutoff for an assay for B19V DNA blood donor screening should, therefore, be approximately 1,000 IU/ml plasma, if a general screening of blood donors for single donation blood components is considered at all: for the overwhelming majority of transfusion recipients, B19V infection is not relevant as well as for the blood donors. B19V DNA screening of vulnerable patients after transfusion seems to be a more reasonable approach than general blood donor screening.

Diagnostic Potential of Imaging Flow Cytometry.

Imaging flow cytometry (IFC) captures multichannel images of hundreds of thousands of single cells within minutes. IFC is seeing a paradigm shift from low- to high-information-content analysis, driven partly by deep learning algorithms. We predict a wealth of applications with potential translation into clinical practice.

Data-analysis strategies for image-based cell profiling.

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.

Cardiac Function Improvement and Bone Marrow Response -: Outcome Analysis of the Randomized PERFECT Phase III Clinical Trial of Intramyocardial CD133+ Application After Myocardial Infarction.

OBJECTIVE: The phase III clinical trial PERFECT was designed to assess clinical safety and efficacy of intramyocardial CD133+ bone marrow stem cell treatment combined with CABG for induction of cardiac repair. DESIGN: Multicentre, double-blinded, randomised placebo controlled trial. SETTING: The study was conducted across six centres in Germany October 2009 through March 2016 and stopped due slow recruitment after positive interim analysis in March 2015. PARTICIPANTS: Post-infarction patients with chronic ischemia and reduced LVEF (25-50%). INTERVENTIONS: Eighty-two patients were randomised to two groups receiving intramyocardial application of 5ml placebo or a suspension of 0.5-5×106 CD133+. OUTCOME: Primary endpoint was delta (∆) LVEF at 180days (d) compared to baseline measured in MRI. FINDINGS (PRESPECIFIED): Safety (n=77): 180d survival was 100%, MACE n=2, SAE n=49, without difference between placebo and CD133+. Efficacy (n=58): The LVEF improved from baseline LVEF 33.5% by +9.6% at 180d, p=0.001 (n=58). Treatment groups were not different in ∆LVEF (ANCOVA: Placebo +8.8% vs. CD133+ +10.4%, ∆CD133+vs placebo +2.6%, p=0.4). FINDINGS (POST HOC): Responders (R) classified by ∆LVEF≥5% after 180d were 60% of the patients (35/58) in both treatment groups. ∆LVEF in ANCOVA was +17.1% in (R) vs. non-responders (NR) (∆LVEF 0%, n=23). NR were characterized by a preoperative response signature in peripheral blood with reduced CD133+ EPC (RvsNR: p=0.005) and thrombocytes (p=0.004) in contrast to increased Erythropoeitin (p=0.02), and SH2B3 mRNA expression (p=0.073). Actuarial computed mean survival time was 76.9±3.32months (R) vs. +72.3±5.0months (NR), HR 0.3 [Cl 0.07-1.2]; p=0.067.Using a machine learning 20 biomarker response parameters were identified allowing preoperative discrimination with an accuracy of 80% (R) and 84% (NR) after 10-fold cross-validation. INTERPRETATION: The PERFECT trial analysis demonstrates that the regulation of induced cardiac repair is linked to the circulating pool of CD133+ EPC and thrombocytes, associated with SH2B3 gene expression. Based on these findings, responders to cardiac functional improvement may be identified by a peripheral blood biomarker signature. TRIAL REGISTRATION: ClinicalTrials.govNCT00950274.

Infectivity of blood products containing cytomegalovirus DNA: results of a lookback study in nonimmunocompromised patients.

BACKGROUND: DNA of human cytomegalovirus (CMV) is frequently detected in plasma of donors with primary CMV infection. It is unknown, however, whether leukoreduced blood products from these donors contain sufficient amounts of infectious virus to cause transfusion-transmitted CMV infections (TT-CMV). STUDY DESIGN AND METHODS: During a 14-year period, CMV DNA-positive donations were identified as part of several previously published studies. Additionally, further donors with seroconversion were tested for CMV DNA. The serostatus of patients who had received a CMV DNA-positive blood product was determined out of pretransfusion samples. Later samples were examined for development of CMV antibodies. Patients with a follow-up of less than 140 days were also tested for CMV DNA. RESULTS: A total of 221 blood products from CMV DNA-positive donations were transfused to 219 recipients. Pretransfusion samples were available for 179 patients, of whom 62 (34.6%) were seronegative. For 39 seronegative recipients of 40 blood products follow-up samples drawn at least 30 days after transfusion were available. The median duration of follow-up was 287 days (range, 38-3784 days). Thirty-six patients were still CMV seronegative in their last sample. Three patients were CMV seropositive due to passive antibody transfer by plasma rich products from seropositive donors, but CMV DNA negative in all tested samples. CONCLUSION: TT-CMV was excluded in all recipients of 40 blood products from CMV DNA-positive donations. This corresponds to a 95% interval of confidence for the risk of TT-CMV of less than 7.4%. Because no patient belonged to a typical at-risk population, the results are only valid for immunocompetent subjects.

Cytomegalovirus Serostatus as Predictor for Adverse Events After Cardiac Surgery: A Prospective Observational Study.

OBJECTIVE: To clarify whether reactivated cytomegalovirus (CMV) infections in critically ill patients lead to worse outcome or just identify more severely ill patients. If CMV has a pathogenic role, latently infected (CMV-seropositive) patients should have worse outcome than seronegative patients because only seropositive patients can experience a CMV reactivation. DESIGN: Post-hoc analysis of a prospective observational study. SETTING: Single university hospital. PARTICIPANTS: The study comprised 983 consecutive patients scheduled for on-pump surgery. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: CMV antibodies were analyzed in preoperative plasma samples. Postoperative adverse events (reintubation, low cardiac output or reinfarction, dialysis, stroke) and 30-day and 1-year mortality were evaluated prospectively. The plasma of reintubated patients and matched control patients was tested for CMV deoxyribonucleic acid, and 618 patients were found to be seropositive for CMV (63%). Among these, the risk for reintubation was increased (10% v 4%, p = 0.001). This increase remained significant after correction for confounding factors (odds ratio 2.70, p = 0.003) and was detectable from the third postoperative day throughout the whole postoperative period. Other outcome parameters were not different. Reintubated seropositive patients were more frequently CMV deoxyribonucleic acid-positive than were matched control patients (40% v 8%, p<0.001). CONCLUSIONS: CMV-seropositive patients had an increased risk of reintubation after cardiac surgery, which was associated with reactivations of their CMV infections. Additional studies should determine whether this complication may be prevented by monitoring of latently infected patients and administering antiviral treatment for reactivated CMV infections.

An open-source solution for advanced imaging flow cytometry data analysis using machine learning.

Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary .cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using "user-friendly" platforms such as CellProfiler Analyst. Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data sets. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye that include subtle measured differences in label free detection channels such as bright-field and dark-field imagery.

Comparison of Two Test Strategies for Clarification of Reactive Results for Anti-HBc in Blood Donors.

OBJECTIVE: Testing for antibodies against hepatitis B core antigen (anti-HBc) was introduced to detect blood donors suffering from occult hepatitis B infection. Confirmation of specification of reactive results in the anti-HBc screening assay is still a challenge for blood donation services. METHODS: Two different test strategies for confirmation of specification of reactive anti-HBc tests, one performed in our institute and one suggested by the German authority (Paul-Ehrlich-Institut (PEI)), were compared. The first strategy is based on one supplemental anti-HBc test, the other requires two supplemental anti-HBc tests. RESULTS: 389 samples from 242 donors were considered. Both test strategies yielded concordant results in 117 reactive samples termed 'true-positive' or 'specificity confirmed', in 156 reactive samples termed 'false-positive' or 'specificity not confirmed', and in 99 negative samples. In 17 samples obtained from 11 donors, both test strategies gave discrepant results ('false-positive' but 'specificity confirmed'). In 10 of 11 donors, a real HBV infection was very unlikely, one remained unclear. 30 donors considered 'false-positive' became negative in all anti-HBc tests after follow-up testing and thus eligible for donor re-entry. CONCLUSIONS: The test strategy suggested by the PEI yielded no additional information but induced an overestimation of HBV infections and unnecessary look-back procedures. Many anti-HBc-reactive donors can be regained after follow-up testing.

Label-free cell cycle analysis for high-throughput imaging flow cytometry.

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.

Fluctuations of hi-hat timing and dynamics in a virtuoso drum track of a popular music recording.

Long-range correlated temporal fluctuations in the beats of musical rhythms are an inevitable consequence of human action. According to recent studies, such fluctuations also lead to a favored listening experience. The scaling laws of amplitude variations in rhythms, however, are widely unknown. Here we use highly sensitive onset detection and time series analysis to study the amplitude and temporal fluctuations of Jeff Porcaro's one-handed hi-hat pattern in "I Keep Forgettin'"-one of the most renowned 16th note patterns in modern drumming. We show that fluctuations of hi-hat amplitudes and interbeat intervals (times between hits) have clear long-range correlations and short-range anticorrelations separated by a characteristic time scale. In addition, we detect subtle features in Porcaro's drumming such as small drifts in the 16th note pulse and non-trivial periodic two-bar patterns in both hi-hat amplitudes and intervals. Through this investigation we introduce a step towards statistical studies of the 20th and 21st century music recordings in the framework of complex systems. Our analysis has direct applications to the development of drum machines and to drumming pedagogy.

CDy6, a photostable probe for long-term real-time visualization of mitosis and proliferating cells.

Long-term real-time visualization of lysosomal dynamics has been challenging at the onset of mitosis due to the lack of fluorescent probes enabling convenient imaging of dividing cells. We developed a long-term real-time photostable mitotic or proliferating marker, CDy6, a BODIPY-derived compound of designation yellow 6, which labels lysosome. In long-term real-time, CDy6 displayed a sharp increase in intensity and change in localization in mitosis, improved photostability, and decreased toxicity compared with other widely used lysosomal and DNA markers, and the ability to label cells in mouse xenograft models. Therefore, CDy6 may open new possibilities to target and trace lysosomal contents during mitosis and to monitor cell proliferation, which can further our knowledge of the basic underlying biological mechanisms in the management of cancer.

Long-range correlation properties in timing of skilled piano performance: the influence of auditory feedback and deep brain stimulation.

Unintentional timing deviations during musical performance can be conceived of as timing errors. However, recent research on humanizing computer-generated music has demonstrated that timing fluctuations that exhibit long-range temporal correlations (LRTC) are preferred by human listeners. This preference can be accounted for by the ubiquitous presence of LRTC in human tapping and rhythmic performances. Interestingly, the manifestation of LRTC in tapping behavior seems to be driven in a subject-specific manner by the LRTC properties of resting-state background cortical oscillatory activity. In this framework, the current study aimed to investigate whether propagation of timing deviations during the skilled, memorized piano performance (without metronome) of 17 professional pianists exhibits LRTC and whether the structure of the correlations is influenced by the presence or absence of auditory feedback. As an additional goal, we set out to investigate the influence of altering the dynamics along the cortico-basal-ganglia-thalamo-cortical network via deep brain stimulation (DBS) on the LRTC properties of musical performance. Specifically, we investigated temporal deviations during the skilled piano performance of a non-professional pianist who was treated with subthalamic-deep brain stimulation (STN-DBS) due to severe Parkinson's disease, with predominant tremor affecting his right upper extremity. In the tremor-affected right hand, the timing fluctuations of the performance exhibited random correlations with DBS OFF. By contrast, DBS restored long-range dependency in the temporal fluctuations, corresponding with the general motor improvement on DBS. Overall, the present investigations demonstrate the presence of LRTC in skilled piano performances, indicating that unintentional temporal deviations are correlated over a wide range of time scales. This phenomenon is stable after removal of the auditory feedback, but is altered by STN-DBS, which suggests that cortico-basal ganglia-thalamocortical circuits play a role in the modulation of the serial correlations of timing fluctuations exhibited in skilled musical performance.