Clonal hematopoiesis and its impact on the aging osteo-hematopoietic niche

Clonal hematopoiesis (CH) defines a premalignant state predominantly found in older persons that increases the risk of developing hematologic malignancies and age-related inflammatory diseases. However, the risk for malignant transformation or non-malignant disorders is variable and difficult to predict, and defining the clinical relevance of specific candidate driver mutations in individual carriers has proved to be challenging. In addition to the cell-intrinsic mechanisms, mutant cells rely on and alter cell-extrinsic factors from the bone marrow (BM) niche, which complicates the prediction of a mutant cell's fate in a shifting pre-malignant microenvironment. Therefore, identifying the insidious and potentially broad impact of driver mutations on supportive niches and immune function in CH aims to understand the subtle differences that enable driver mutations to yield different clinical outcomes. Here, we review the changes in the aging BM niche and the emerging evidence supporting the concept that CH can progressively alter components of the local BM microenvironment. These alterations may have profound implications for the functionality of the osteo-hematopoietic niche and overall bone health, consequently fostering a conducive environment for the continued development and progression of CH. We also provide an overview of the latest technology developments to study the spatiotemporal dependencies in the CH BM niche, ideally in the context of longitudinal studies following CH over time. Finally, we discuss aspects of CH carrier management in clinical practice, based on work from our group and others.

9-Month observational Dia-Vacc study of vaccine type influence on SARS-CoV-2 immunity in dialysis and kidney transplant patients

BACKGROUND

SARS-CoV-2mRNA vaccination related seroconversion rates are reduced in dialysis and kidney transplant patients.

METHODS

We evaluated nine months follow up data in our observational Dia-Vacc study exploring specific cellular (interferon-γ release assay) or/and humoral immune responses after 2x SARS-CoV-2mRNA vaccination in 880 participants including healthy medical personnel (125-MP), dialysis patients (595-DP), kidney transplant recipients (111-KTR), and apheresis patients (49-AP) with positive seroconversion (de novo IgA or IgG antibody positivity by ELISA) after eight weeks.

FINDINGS

Nine months after first vaccination, receptor binding domain (RBD) antibodies were still positive in 90 % of MP, 86 % of AP, but only 55 %/48 % of DP/KTR, respectively. Seroconversion remained positive in 100 % of AP and 99·2 % of MP, but 86 %/81 % of DP/KTR, respectively. Compared to MP, DP but not KTR or AP were at risk for a strong RBD decline, while KTR kept lowest RBD values over time. By multivariate analysis, BNT162b2mRNA versus 1273-mRNA vaccine type was an independent risk factor for a strong decline of RBD antibodies. Within the DP group, only time on dialysis was another (inverse) risk factor for the DP group. Compared to humoral immunity, T-cell immunity decline was less prominent.

INTERPRETATION

While seroconverted KTR reach lowest RBD values over time, DP are at specific risk for a strong decline of RBD antibodies after successful SARS-CoV-2mRNA vaccination, which also depends on the vaccine type being used. Therefore, booster vaccinations for DP should be considered earlier compared to normal population.

Stratifying esophago-gastric cancer treatment using a patient-derived organoid-based threshold

BACKGROUND AND AIMS

This study sought to determine the value of patient-derived organoids (PDOs) from esophago-gastric adenocarcinoma (EGC) for response prediction to neoadjuvant chemotherapy (neoCTx).

METHODS

Endoscopic biopsies of patients with locally advanced EGC (n = 120) were taken into culture and PDOs expanded. PDOs' response towards the single substances of the FLOT regimen and the combination treatment were correlated to patients' pathological response using tumor regression grading. A classifier based on FLOT response of PDOs was established in an exploratory cohort (n = 13) and subsequently confirmed in an independent validation cohort (n = 13).

RESULTS

EGC PDOs reflected patients' diverse responses to single chemotherapeutics and the combination regimen FLOT. In the exploratory cohort, PDOs response to single 5-FU and FLOT combination treatment correlated with the patients' pathological response (5-FU: Kendall's τ = 0.411, P = 0.001; FLOT: Kendall's τ = 0.694, P = 2.541e-08). For FLOT testing, a high diagnostic precision in receiver operating characteristic (ROC) analysis was reached with an AUCROC of 0.994 (CI 0.980 to 1.000). The discriminative ability of PDO-based FLOT testing allowed the definition of a threshold, which classified in an independent validation cohort FLOT responders from non-responders with high sensitivity (90%), specificity (100%) and accuracy (92%).

CONCLUSION

In vitro drug testing of EGC PDOs has a high predictive accuracy in classifying patients' histological response to neoadjuvant FLOT treatment. Taking into account the high rate of successful PDO expansion from biopsies, the definition of a threshold that allows treatment stratification paves the way for an interventional trial exploring PDO-guided treatment of EGC patients.

Data integration between clinical research and patient care: A framework for context-depending data sharing and in silico predictions

The transfer of new insights from basic or clinical research into clinical routine is usually a lengthy and time-consuming process. Conversely, there are still many barriers to directly provide and use routine data in the context of basic and clinical research. In particular, no coherent software solution is available that allows a convenient and immediate bidirectional transfer of data between concrete treatment contexts and research settings. Here, we present a generic framework that integrates health data (e.g., clinical, molecular) and computational analytics (e.g., model predictions, statistical evaluations, visualizations) into a clinical software solution which simultaneously supports both patient-specific healthcare decisions and research efforts, while also adhering to the requirements for data protection and data quality. Specifically, our work is based on a recently established generic data management concept, for which we designed and implemented a web-based software framework that integrates data analysis, visualization as well as computer simulation and model prediction with audit trail functionality and a regulation-compliant pseudonymization service. Within the front-end application, we established two tailored views: a clinical (i.e., treatment context) perspective focusing on patient-specific data visualization, analysis and outcome prediction and a research perspective focusing on the exploration of pseudonymized data. We illustrate the application of our generic framework by two use-cases from the field of haematology/oncology. Our implementation demonstrates the feasibility of an integrated generation and backward propagation of data analysis results and model predictions at an individual patient level into clinical decision-making processes while enabling seamless integration into a clinical information system or an electronic health record.

Modelling of immune response in chronic myeloid leukemia patients suggests potential for treatment reduction prior to cessation

Introduction

Discontinuation of tyrosine kinase inhibitor (TKI) treatment is emerging as the main therapy goal for Chronic Myeloid Leukemia (CML) patients. The DESTINY trial showed that TKI dose reduction prior to cessation can lead to an increased number of patients achieving sustained treatment free remission (TFR). However, there has been no systematic investigation to evaluate how dose reduction regimens can further improve the success of TKI stop trials.

Methods

Here, we apply an established mathematical model of CML therapy to investigate different TKI dose reduction schemes prior to therapy cessation and evaluate them with respect to the total amount of drug used and the expected TFR success.

Results

Our systematic analysis confirms clinical findings that the overall time of TKI treatment is a major determinant of TFR success, while highlighting that lower dose TKI treatment for the same duration is equally sufficient for many patients. Our results further suggest that a stepwise dose reduction prior to TKI cessation can increase the success rate of TFR, while substantially reducing the amount of administered TKI.

Discussion

Our findings illustrate the potential of dose reduction schemes prior to treatment cessation and suggest corresponding and clinically testable strategies that are applicable to many CML patients.

Impact of BCR::ABL1 transcript type on RT-qPCR amplification performance and molecular response to therapy

Several studies have reported that chronic myeloid leukaemia (CML) patients expressing e14a2 BCR::ABL1 have a faster molecular response to therapy compared to patients expressing e13a2. To explore the reason for this difference we undertook a detailed technical comparison of the commonly used Europe Against Cancer (EAC) BCR::ABL1 reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) assay in European Treatment and Outcome Study (EUTOS) reference laboratories (n = 10). We found the amplification ratio of the e13a2 amplicon was 38% greater than e14a2 (p = 0.015), and the amplification efficiency was 2% greater (P = 0.17). This subtle difference led to measurable transcript-type dependent variation in estimates of residual disease which could be corrected by (i) taking the qPCR amplification efficiency into account, (ii) using alternative RT-qPCR approaches or (iii) droplet digital PCR (ddPCR), a technique which is relatively insensitive to differences in amplification kinetics. In CML patients, higher levels of BCR::ABL1/GUSB were identified at diagnosis for patients expressing e13a2 (n = 67) compared to e14a2 (n = 78) when analysed by RT-qPCR (P = 0.0005) but not ddPCR (P = 0.5). These data indicate that widely used RT-qPCR assays result in subtly different estimates of disease depending on BCR::ABL1 transcript type; these differences are small but may need to be considered for optimal patient management.

RNAi-Mediated Screen of Primary AML Cells Nominates MDM4 as a Therapeutic Target in NK-AML with DNMT3A Mutations

DNA-methyltransferase 3A (DNMT3A) mutations belong to the most frequent genetic aberrations found in adult acute myeloid leukemia (AML). Recent evidence suggests that these mutations arise early in leukemogenesis, marking leukemic progenitors and stem cells, and persist through consolidation chemotherapy, providing a pool for AML relapse. Currently, there are no therapeutic approaches directed specifically against this cell population. To unravel therapeutically actionable targets in mutant DNMT3A-driven AML cells, we have performed a focused RNAi screen in a panel of 30 primary AML samples, all carrying a DNMT3A R882 mutation. As one of the strongest hits, we identified MDM4 as a gene essential for proliferation of primary DNMT3A^WT/R882X AML cells. We analyzed a publicly available RNA-Seq dataset of primary normal karyotype (NK) AML samples and found a trend towards MDM4 transcript overexpression particularly in DNMT3A-mutant samples. Moreover, we found that the MDM2/4 inhibitor ALRN-6924 impairs growth of DNMT3A^WT/R882X primary cells in vitro by inducing cell cycle arrest through upregulation of p53 target genes. Our results suggest that MDM4 inhibition is a potential target in NK-AML patients bearing DNMT3A R882X mutations.

Comparative Gene Expression Analysis Reveals Similarities and Differences of Chronic Myeloid Leukemia Phases

Chronic myeloid leukemia (CML) is a slowly progressing blood cancer that primarily affects elderly people. Without successful treatment, CML progressively develops from the chronic phase through the accelerated phase to the blast crisis, and ultimately to death. Nowadays, the availability of targeted tyrosine kinase inhibitor (TKI) therapies has led to long-term disease control for the vast majority of patients. Nevertheless, there are still patients that do not respond well enough to TKI therapies and available targeted therapies are also less efficient for patients in accelerated phase or blast crises. Thus, a more detailed characterization of molecular alterations that distinguish the different CML phases is still very important. We performed an in-depth bioinformatics analysis of publicly available gene expression profiles of the three CML phases. Pairwise comparisons revealed many differentially expressed genes that formed a characteristic gene expression signature, which clearly distinguished the three CML phases. Signaling pathway expression patterns were very similar between the three phases but differed strongly in the number of affected genes, which increased with the phase. Still, significant alterations of MAPK, VEGF, PI3K-Akt, adherens junction and cytokine receptor interaction signaling distinguished specific phases. Our study also suggests that one can consider the phase-wise CML development as a three rather than a two-step process. This is in accordance with the phase-specific expression behavior of 24 potential major regulators that we predicted by a network-based approach. Several of these genes are known to be involved in the accumulation of additional mutations, alterations of immune responses, deregulation of signaling pathways or may have an impact on treatment response and survival. Importantly, some of these genes have already been reported in relation to CML (e.g., AURKB, AZU1, HLA-B, HLA-DMB, PF4) and others have been found to play important roles in different leukemias (e.g., CDCA3, RPL18A, PRG3, TLX3). In addition, increased expression of BCL2 in the accelerated and blast phase indicates that venetoclax could be a potential treatment option. Moreover, a characteristic signaling pathway signature with increased expression of cytokine and ECM receptor interaction pathway genes distinguished imatinib-resistant patients from each individual CML phase. Overall, our comparative analysis contributes to an in-depth molecular characterization of similarities and differences of the CML phases and provides hints for the identification of patients that may not profit from an imatinib therapy, which could support the development of additional treatment strategies.

How to predict relapse in leukemia using time series data: A comparative in silico study

Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improve the predictions for patient-specific treatment responses. We designed a synthetic experiment simulating response kinetics of 5,000 patients to compare different computational methods with respect to their ability to accurately predict relapse for chronic and acute myeloid leukemia treatment. Technically, we used clinical reference data to first fit a model and then generate de novo model simulations of individual patients' time courses for which we can systematically tune data quality (i.e. measurement error) and quantity (i.e. number of measurements). Based hereon, we compared the prediction accuracy of three different computational methods, namely mechanistic models, generalized linear models, and deep neural networks that have been fitted to the reference data. Reaching prediction accuracies between 60 and close to 100%, our results indicate that data quality has a higher impact on prediction accuracy than the specific choice of the particular method. We further show that adapted treatment and measurement schemes can considerably improve the prediction accuracy by 10 to 20%. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukemia patients.

Noncanonical effector functions of the T-memory-like T-PLL cell are shaped by cooperative TCL1A and TCR signaling

T-cell prolymphocytic leukemia (T-PLL) is a poor-prognostic neoplasm. Differentiation stage and immune-effector functions of the underlying tumor cell are insufficiently characterized. Constitutive activation of the T-cell leukemia 1A (TCL1A) oncogene distinguishes the (pre)leukemic cell from regular postthymic T cells. We assessed activation-response patterns of the T-PLL lymphocyte and interrogated the modulatory impact by TCL1A. Immunophenotypic and gene expression profiles revealed a unique spectrum of memory-type differentiation of T-PLL with predominant central-memory stages and frequent noncanonical patterns. Virtually all T-PLL expressed a T-cell receptor (TCR) and/or CD28-coreceptor without overrepresentation of specific TCR clonotypes. The highly activated leukemic cells also revealed losses of negative-regulatory TCR coreceptors (eg, CTLA4). TCR stimulation of T-PLL cells evoked higher-than-normal cell-cycle transition and profiles of cytokine release that resembled those of normal memory T cells. More activated phenotypes and higher TCL1A correlated with inferior clinical outcomes. TCL1A was linked to the marked resistance of T-PLL to activation- and FAS-induced cell death. Enforced TCL1A enhanced phospho-activation of TCR kinases, second-messenger generation, and JAK/STAT or NFAT transcriptional responses. This reduced the input thresholds for IL-2 secretion in a sensitizer-like fashion. Mice of TCL1A-initiated protracted T-PLL development resembled such features. When equipped with epitope-defined TCRs or chimeric antigen receptors, these Lckpr-hTCL1Atg T cells gained a leukemogenic growth advantage in scenarios of receptor stimulation. Overall, we propose a model of T-PLL pathogenesis in which TCL1A enhances TCR signals and drives the accumulation of death-resistant memory-type cells that use amplified low-level stimulatory input, and whose loss of negative coregulators additionally maintains their activated state. Treatment rationales are provided by combined interception in TCR and survival signaling.

Overlooking the obvious? On the potential of treatment alterations to predict patient-specific therapy response

Prognostic or therapeutic classification of diseases is often based on clinical or genetic characteristics at diagnosis or response landmarks determined at a certain time point of treatment. On the other hand, there are more and more means, such as molecular markers and sensor data, that allow for quantification of disease or therapeutic parameters over time. Although a general value of time-resolved disease monitoring is widely accepted, the full potential of using the available information on disease and treatment dynamics in the context of outcome prediction or individualized treatment optimization still seems to be, at least partially, overlooked. Within this Perspective, we summarize the conceptual idea of using dynamic information to obtain a better understanding of complex pathophysiological processes within their particular "host environment," which also allows us to intrinsically map patient-specific heterogeneity. Specifically, we discuss to which extent treatment alterations can provide additional information to understand a patient's individual condition and use this information to further adapt the therapeutic strategy. This conceptual discussion is illustrated by using examples from myeloid leukemias to which we recently applied this concept using statistical and mathematical modeling.

Differential response to cytotoxic therapy explains treatment dynamics of acute myeloid leukaemia patients: insights from a mathematical modelling approach

Disease response and durability of remission are very heterogeneous in patients with acute myeloid leukaemia (AML). There is increasing evidence that the individual risk of early relapse can be predicted based on the initial treatment response. However, it is unclear how such a correlation is linked to functional aspects of AML progression and treatment. We suggest a mathematical model in which leukaemia-initiating cells and normal/healthy haematopoietic stem and progenitor cells reversibly change between an active state characterized by proliferation and chemosensitivity and a quiescent state, in which the cells do not divide, but are also insensitive to chemotherapy. Applying this model to 275 molecular time courses of nucleophosmin 1-mutated patients, we conclude that the differential chemosensitivity of the leukaemia-initiating cells together with the cells' intrinsic proliferative capacity is sufficient to reproduce both, early relapse as well as long-lasting remission. We can, furthermore, show that the model parameters associated with individual chemosensitivity and proliferative advantage of the leukaemic cells are closely linked to the patients' time to relapse, while a reliable prediction based on early response only is not possible based on the currently available data. Although we demonstrate with our approach, that the complete response data is sufficient to quantify the aggressiveness of the disease, further investigations are necessary to study how an intensive early sampling strategy may prospectively improve risk assessment and help to optimize individual treatments.

Analysis of chronic myeloid leukaemia during deep molecular response by genomic PCR: a traffic light stratification model with impact on treatment-free remission

This work investigated patient-specific genomic BCR-ABL1 fusions as markers of measurable residual disease (MRD) in chronic myeloid leukaemia, with a focus on relevance to treatment-free remission (TFR) after achievement of deep molecular response (DMR) on tyrosine kinase inhibitor (TKI) therapy. DNA and mRNA BCR-ABL1 measurements by qPCR were compared in 2189 samples (129 patients) and by digital PCR in 1279 sample (62 patients). A high correlation was found at levels of disease above MR4, but there was a poor correlation for samples during DMR. A combination of DNA and RNA MRD measurements resulted in a better prediction of molecular relapse-free survival (MRFS) after TKI stop (n = 17) or scheduled interruption (n = 25). At 18 months after treatment cessation, patients with stopped or interrupted TKI therapy who were DNA negative/RNA negative during DMR maintenance (green group) had an MRFS of 80% and 100%, respectively, compared with those who were DNA positive/RNA negative (MRFS = 57% and 67%, respectively; yellow group) or DNA positive/RNA positive (MRFS = 20% for both cohorts; red group). Thus, we propose a "traffic light" stratification as a TFR predictor based on DNA and mRNA BCR-ABL1 measurements during DMR maintenance before TKI cessation.

Survival differences and associated molecular signatures of DNMT3A-mutant acute myeloid leukemia patients

Acute myeloid leukemia (AML) is a very heterogeneous and highly malignant blood cancer. Mutations of the DNA methyltransferase DNMT3A are among the most frequent recurrent genetic lesions in AML. The majority of DNMT3A-mutant AML patients shows fast relapse and poor survival, but also patients with long survival or long-term remission have been reported. Underlying molecular signatures and mechanisms that contribute to these survival differences are only poorly understood and have not been studied in detail so far. We applied hierarchical clustering to somatic gene mutation profiles of 51 DNMT3A-mutant patients from The Cancer Genome Atlas (TCGA) AML cohort revealing two robust patient subgroups with profound differences in survival. We further determined molecular signatures that distinguish both subgroups. Our results suggest that FLT3 and/or NPM1 mutations contribute to survival differences of DNMT3A-mutant patients. We observed an upregulation of genes of the p53, VEGF and DNA replication pathway and a downregulation of genes of the PI3K-Akt pathway in short- compared to long-lived patients. We identified that the majority of measured miRNAs was downregulated in the short-lived group and we found differentially expressed microRNAs between both subgroups that have not been reported for AML so far (miR-153-2, miR-3065, miR-95, miR-6718) suggesting that miRNAs could be important for prognosis. In addition, we learned gene regulatory networks to predict potential major regulators and found several genes and miRNAs with known roles in AML pathogenesis, but also interesting novel candidates involved in the regulation of hematopoiesis, cell cycle, cell differentiation, and immunity that may contribute to the observed survival differences of both subgroups and could therefore be important for prognosis. Moreover, the characteristic gene mutation and expression signatures that distinguished short- from long-lived patients were also predictive for independent DNMT3A-mutant AML patients from other cohorts and could also contribute to further improve the European LeukemiaNet (ELN) prognostic scoring system. Our study represents the first in-depth computational approach to identify molecular factors associated with survival differences of DNMT3A-mutant AML patients and could trigger additional studies to develop robust molecular markers for a better stratification of AML patients with DNMT3A mutations.

Domain-specific cues improve robustness of deep learning-based segmentation of CT volumes

Machine learning has considerably improved medical image analysis in the past years. Although data-driven approaches are intrinsically adaptive and thus, generic, they often do not perform the same way on data from different imaging modalities. In particular computed tomography (CT) data poses many challenges to medical image segmentation based on convolutional neural networks (CNNs), mostly due to the broad dynamic range of intensities and the varying number of recorded slices of CT volumes. In this paper, we address these issues with a framework that adds domain-specific data preprocessing and augmentation to state-of-the-art CNN architectures. Our major focus is to stabilise the prediction performance over samples as a mandatory requirement for use in automated and semi-automated workflows in the clinical environment. To validate the architecture-independent effects of our approach we compare a neural architecture based on dilated convolutions for parallel multi-scale processing (a modified Mixed-Scale Dense Network: MS-D Net) to traditional scaling operations (a modified U-Net). Finally, we show that an ensemble model combines the strengths across different individual methods. Our framework is simple to implement into existing deep learning pipelines for CT analysis. It performs well on a range of tasks such as liver and kidney segmentation, without significant differences in prediction performance on strongly differing volume sizes and varying slice thickness. Thus our framework is an essential step towards performing robust segmentation of unknown real-world samples.

Proline-rich protein PRR19 functions with cyclin-like CNTD1 to promote meiotic crossing over in mouse

Orderly chromosome segregation is enabled by crossovers between homologous chromosomes in the first meiotic division. Crossovers arise from recombination-mediated repair of programmed DNA double-strand breaks (DSBs). Multiple DSBs initiate recombination, and most are repaired without crossover formation, although one or more generate crossovers on each chromosome. Although the underlying mechanisms are ill-defined, the differentiation and maturation of crossover-specific recombination intermediates requires the cyclin-like CNTD1. Here, we identify PRR19 as a partner of CNTD1. We find that, like CNTD1, PRR19 is required for timely DSB repair and the formation of crossover-specific recombination complexes. PRR19 and CNTD1 co-localise at crossover sites, physically interact, and are interdependent for accumulation, indicating a PRR19-CNTD1 partnership in crossing over. Further, we show that CNTD1 interacts with a cyclin-dependent kinase, CDK2, which also accumulates in crossover-specific recombination complexes. Thus, the PRR19-CNTD1 complex may enable crossover differentiation by regulating CDK2.

Urinary MicroRNAs as Potential Markers for Non-Invasive Diagnosis of Bladder Cancer

Currently, voided urine cytology (VUC) serves as the gold standard for the detection of bladder cancer (BCa) in urine. Despite its high specificity, VUC has shortcomings in terms of sensitivity. Therefore, alternative biomarkers are being searched, which might overcome these disadvantages as a useful adjunct to VUC. The aim of this study was to evaluate the diagnostic potential of the urinary levels of selected microRNAs (miRs), which might represent such alternative biomarkers due to their BCa-specific expression. Expression levels of nine BCa-associated microRNAs (miR-21, -96, -125b, -126, -145, -183, -205, -210, -221) were assessed by quantitative PCR in urine sediments from 104 patients with primary BCa and 46 control subjects. Receiver operating characteristic (ROC) curve analyses revealed a diagnostic potential for miR-96, -125b, -126, -145, -183, and -221 with area under the curve (AUC) values between 0.605 and 0.772. The combination of the four best candidates resulted in sensitivity, specificity, positive and negative predictive values (NPV), and accuracy of 73.1%, 95.7%, 97.4%, 61.1%, and 80.0%, respectively. Combined with VUC, sensitivity and NPV could be increased by nearly 8%, each surpassing the performance of VUC alone. The present findings suggested a diagnostic potential of miR-125b, -145, -183, and -221 in combination with VUC for non-invasive detection of BCa in urine.

Identifying Genetic Lesions in Ocular Adnexal Extranodal Marginal Zone Lymphomas of the MALT Subtype by Whole Genome, Whole Exome and Targeted Sequencing

The pathogenesis of ocular adnexal marginal zone lymphomas of mucosa-associated lymphatic tissue-type (OAML) is not fully understood. We performed whole genome sequencing (WGS) and/or whole exome sequencing (WES) for 13 cases of OAML and sequenced 38 genes selected from this analysis in a large cohort of 82 OAML. Besides confirmation of frequent mutations in the genes transducin beta like 1 X-linked receptor 1 (TBL1XR1) and cAMP response element binding protein (CREBBP), we newly identifed JAK3 as a frequently mutated gene in OAML (11% of cases). In our retrospective cohort, JAK3 mutant cases had a shorter progression-free survival compared with unmutated cases. Other newly identified genes recurrently mutated in 5-10% of cases included members of the collagen family (collagen type XII alpha 1/2 (COL12A1, COL1A2)) and DOCK8. Evaluation of the WGS data of six OAML did not reveal translocations or a current infection of the lymphoma cells by viruses. Evaluation of the WGS data for copy number aberrations confirmed frequent loss of TNFAIP3, and revealed recurrent gains of the NOTCH target HES4, and of members of the CEBP transcription factor family. Overall, we identified several novel genes recurrently affected by point mutations or copy number alterations, but our study also indicated that the landscape of frequently (>10% of cases) mutated protein-coding genes in OAML is now largely known.

Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology

Background

Individualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities.

Results

In the following we describe a software framework that facilitates the integration of mathematical models and computer simulations into routine clinical processes to support decision-making. This is achieved by combining standard data management and data exploration tools, with the generation and visualization of mathematical model predictions for treatment options at an individual patient level.

Conclusions

By integrating model results in an audit trail compatible manner into established clinical workflows, our framework has the potential to foster the use of systems-medical approaches in clinical practice. We illustrate the framework application by two use cases from the field of haematological oncology.

Automated detection of the HER2 gene amplification status in Fluorescence in situ hybridization images for the diagnostics of cancer tissues

The human epidermal growth factor receptor 2 (HER2) gene amplification status is a crucial marker for evaluating clinical therapies of breast or gastric cancer. We propose a deep learning-based pipeline for the detection, localization and classification of interphase nuclei depending on their HER2 gene amplification state in Fluorescence in situ hybridization (FISH) images. Our pipeline combines two RetinaNet-based object localization networks which are trained (1) to detect and classify interphase nuclei into distinct classes normal, low-grade and high-grade and (2) to detect and classify FISH signals into distinct classes HER2 or centromere of chromosome 17 (CEN17). By independently classifying each nucleus twice, the two-step pipeline provides both robustness and interpretability for the automated detection of the HER2 amplification status. The accuracy of our deep learning-based pipeline is on par with that of three pathologists and a set of 57 validation images containing several hundreds of nuclei are accurately classified. The automatic pipeline is a first step towards assisting pathologists in evaluating the HER2 status of tumors using FISH images, for analyzing FISH images in retrospective studies, and for optimizing the documentation of each tumor sample by automatically annotating and reporting of the HER2 gene amplification specificities.

The prevalence of headache in German pupils of different ages and school types

Background/objective

Headache in pupils is underestimated and has a negative impact on learning and life. The aim of this study was to investigate headache prevalence and its collateral effects, in pupils of different ages and school types in a German city.

Methods

Anonymized questionnaires were distributed to 5419 pupils attending primary and secondary schools. Demographics, headache frequency, analgesic use, school absence and, for secondary school children, data on lifestyle were collected.

Results

The questionnaire was returned by 2706 children (49%), 1362 (50.3%) girls, 1344 (49.7%) boys. Of these, 36.6% indicated a frequency of 1, and 31.5% a frequency of ≥ 2 headache days per month within the last 3 months. Headache prevalence increased with school grade, age and secondary school type: 63.6%, 67.2% and 79.5% for primary school children, pupils attending 8-year and pupils attending 6-year secondary schools, respectively. With secondary school level I certificates, pupils are prepared for general professional training in 6 years. Secondary school level II results, after 8 years of training, in university entrance level II certificates, which are the precondition for university studies. Girls reported significantly more headache than boys (73% vs. 63.1%). A significant relationship has been observed between headache frequency and school absence and between headache intensity and headache frequency. Of pupils with headache at least twice a month, 48.1% reported analgesic intake. Ibuprofen (49.1%) and paracetamol (32.8%) were the most frequently used analgesics. Of those pupils with headache ≥ 2 days/month, 68.3% did not have a specific headache diagnosis. Concomitant diseases and regular drug intake, analgesic intake for another reason than headache, caffeine consumption and lack of participation in sports were positively correlated with headache.

Conclusions

The majority of pupils suffer from headache at least once a month. Since frequent headache results in educational and social limitations, pupils at risk should be identified and referred to headache education programs. Efforts are needed to improve the management of juvenile headache patients.

Paracrine mechanisms in early differentiation of human pluripotent stem cells: Insights from a mathematical model

With their capability to self-renew and differentiate into derivatives of all three germ layers, human pluripotent stem cells (hPSCs) offer a unique model to study aspects of human development in vitro. Directed differentiation towards mesendodermal lineages is a complex process, involving transition through a primitive streak (PS)-like stage. We have recently shown PS-like patterning from hPSCs into definitive endoderm, cardiac as well as presomitic mesoderm by only modulating the bulk cell density and the concentration of the GSK3 inhibitor CHIR99021, a potent activator of the WNT pathway. The patterning process is modulated by a complex paracrine network, whose identity and mechanistic consequences are poorly understood. To study the underlying dynamics, we here applied mathematical modeling based on ordinary differential equations. We compared time-course data of early hPSC differentiation to increasingly complex model structures with incremental numbers of paracrine factors. Model simulations suggest at least three paracrine factors being required to recapitulate the experimentally observed differentiation kinetics. Feedback mechanisms from both undifferentiated and differentiated cells turned out to be crucial. Evidence from double knock-down experiments and secreted protein enrichment allowed us to hypothesize on the identity of two of the three predicted factors. From a practical perspective, the mathematical model predicts optimal settings for directing lineage-specific differentiation. This opens new avenues for rational stem cell bioprocessing in more advanced culture systems, e.g. in perfusion-fed bioreactors enabling cell therapies.

Reduced tyrosine kinase inhibitor dose is predicted to be as effective as standard dose in chronic myeloid leukemia: a simulation study based on phase III trial data

Continuing tyrosine kinase inhibitor (TKI)-mediated targeting of the BCR-ABL1 oncoprotein is the standard therapy for chronic myeloid leukemia (CML) and allows for a sustained disease control in the majority of patients. While therapy cessation for patients appeared as a safe option for about half of those patients with optimal response, no systematic assessment of long-term TKI dose de-escalation has been made. We use a mathematical model to analyze and consistently describe biphasic treatment responses from TKI-treated patients from two independent clinical phase III trials. Scale estimates reveal that drug efficiency determines the initial response while the long-term behavior is limited by the rare activation of leukemic stem cells. We use this mathematical framework to investigate the influence of different dosing regimens on the treatment outcome. We provide strong evidence to suggest that TKI dose de-escalation (at least 50%) does not lead to a reduction of long-term treatment efficiency for most patients, who have already achieved sustained remission, and maintains the secondary decline of BCR-ABL1 levels. We demonstrate that continuous BCR-ABL1 monitoring provides patient-specific predictions of an optimal reduced dose without decreasing the anti-leukemic effect on residual leukemic stem cells. Our results are consistent with the interim results of the DESTINY trial and provide clinically testable predictions. Our results suggest that dose-halving should be considered as a long-term treatment option for CML patients with good response under continuing maintenance therapy with TKIs. We emphasize the clinical potential of this approach to reduce treatment-related side-effects and treatment costs.

Metabolism is the tie: The Bertalanffy-type cancer growth model as common denominator of various modelling approaches

Cancer or tumour growth has been addressed from a variety of mathematical modelling perspectives in the past. Examples are single variable growth models, reaction diffusion models, compartment models, individual cell-based models, clonal competition models, to name only a few. In this paper, we show that the so called Bertalanffy-type growth model is a macroscopic model variant that can be conceived as an optimal condensed modelling approach that to a high degree preserves complexity with respect to the aforementioned more complex modelling variants. The derivation of the Bertalanffy-type model is crucially based on features of metabolism. Therefore, this model contains a shape parameter that can be interpreted as a resource utilisation efficiency. This shape parameter reflects features that are usually captured in much more complex models. To be specific, the shape parameter is related to morphological structures of tumours, which in turn depend on metabolic conditions. We, furthermore, show that a single variable variant of the Bertalanffy-type model can straightforwardly be extended to a multiclonal competition model. Since competition is crucially based on available shared or clone-specific resources, the metabolism-based approach is an obvious candidate to capture clonal competition. Depending on the specific context, metabolic reprogramming or other oncogene driven changes either lead to a suppression of cancer cells or to an improved competition resulting in outgrowth of tumours. The parametrisation of the Bertalanffy-type growth model allows to account for this observed variety of cancer characteristics. The shape parameter, conceived as a classifier for healthy and oncogenic phenotypes, supplies a link to survival and evolutionary stability concepts discussed in demographic studies, such as opportunistic versus equilibrium strategies.

Neurotrophin Receptor p75NTR Regulates Immune Function of Plasmacytoid Dendritic Cells

Plasmacytoid dendritic cells (pDCs) regulate innate and adaptive immunity. Neurotrophins and their receptors control the function of neuronal tissue. In addition, they have been demonstrated to be part of the immune response but little is known about the effector immune cells involved. We report, for the first time, the expression and immune-regulatory function of the low affinity neurotrophin receptor p75 neurotrophin receptor (p75NTR) by the antigen-presenting pDCs, mediated by toll-like receptor (TLR) 9 activation and differential phosphorylation of interferon regulatory factor 3 and 7. The modulation of p75NTR on pDCs significantly influences disease progression of asthma in an ovalbumin-induced mouse model mediated by the TLR9 signaling pathway. p75NTR activation of pDCs from patients with asthma increased allergen-specific T cell proliferation and cytokine secretion in nerve growth factor concentration-dependent manner. Further, p75NTR activation of pDCs delayed the onset of autoimmune diabetes in RIP-CD80GP mice and aggravated graft-versus-host disease in a xenotransplantation model. Thus, p75NTR signaling on pDCs constitutes a new and critical mechanism connecting neurotrophin signaling and immune response regulation with great therapeutic potential for a variety of immune disorders.

Clonal competition in BcrAbl-driven leukemia: how transplantations can accelerate clonal conversion

Background

Clonal competition in cancer describes the process in which the progeny of a cell clone supersedes or succumbs to other competing clones due to differences in their functional characteristics, mostly based on subsequently acquired mutations. Even though the patterns of those mutations are well explored in many tumors, the dynamical process of clonal selection is underexposed.

Methods

We studied the dynamics of clonal competition in a BcrAbl-induced leukemia using a γ-retroviral vector library encoding the oncogene in conjunction with genetic barcodes. To this end, we studied the growth dynamics of transduced cells on the clonal level both in vitro and in vivo in transplanted mice.

Results

While we detected moderate changes in clonal abundancies in vitro, we observed monoclonal leukemias in 6/30 mice after transplantation, which intriguingly were caused by only two different BcrAbl clones. To analyze the success of these clones, we applied a mathematical model of hematopoietic tissue maintenance, which indicated that a differential engraftment capacity of these two dominant clones provides a possible explanation of our observations. These findings were further supported by additional transplantation experiments and increased BcrAbl transcript levels in both clones.

Conclusion

Our findings show that clonal competition is not an absolute process based on mutations, but highly dependent on selection mechanisms in a given environmental context.

Electronic supplementary material

The online version of this article (doi:10.1186/s12943-017-0668-x) contains supplementary material, which is available to authorized users.

Dissecting mechanisms of mouse embryonic stem cells heterogeneity through a model-based analysis of transcription factor dynamics

Pluripotent mouse embryonic stem cells (mESCs) show heterogeneous expression levels of transcription factors (TFs) involved in pluripotency regulation, among them Nanog and Rex1. The expression of both TFs can change dynamically between states of high and low activity, correlating with the cells' capacity for self-renewal. Stochastic fluctuations as well as sustained oscillations in gene expression are possible mechanisms to explain this behaviour, but the lack of suitable data hampered their clear distinction. Here, we present a systems biology approach in which novel experimental data on TF heterogeneity is complemented by an agent-based model of mESC self-renewal. Because the model accounts for intracellular interactions, cell divisions and heredity structures, it allows for evaluating the consistency of the proposed mechanisms with data on population growth and on TF dynamics after cell sorting. Our model-based analysis revealed that a bistable, noise-driven network model fulfils the minimal requirements to consistently explain Nanog and Rex1 expression dynamics in heterogeneous and sorted mESC populations. Moreover, we studied the impact of TF-related proliferation capacities on the frequency of state transitions and demonstrate that cellular genealogies can provide insights into the heredity structures of mESCs.

Concepts in mature T-cell lymphomas - highlights from an international joint symposium on T-cell immunology and oncology<sup/>

Growing attention in mature T-cell lymphomas/leukemias (MTCL) is committed to more accurate and meaningful classifications, improved pathogenetic concepts and expanded therapeutic options. This requires considerations of the immunologic concepts of T-cell homeostasis and the specifics of T-cell receptor (TCR) affinities and signaling. Scientists from various disciplines established the CONTROL-T research unit and in an international conference on MTCL they brought together experts from T-cell immunity, oncology, immunotherapy and systems biology. We report here meeting highlights on the covered topics of diagnostic pitfalls, implications by the new WHO classification, insights from discovered genomic lesions as well as TCR-centric concepts of cellular dynamics in host defense, auto-immunity and tumorigenic clonal escape, including predictions to be derived from in vivo imaging and mathematical modeling. Presentations on novel treatment approaches were supplemented by strategies of optimizing T-cell immunotherapies. Work packages, that in joint efforts would advance the field of MTCL more efficiently, are identified.

Process-based approach to modeling recurrent-event data explicated on the basis of occurrences of tooth losses in two different prosthetic treatment concepts

Background

In studies comparing different prosthetic treatment concepts the repeated loss of teeth was chosen as the primary outcome. The resulting data appear to represent a data structure of recurrent events. However, the application of an existing method for recurrent events is far from straightforward. Often only the first event or the final state is analyzed using Kaplan–Meier survival statistics, thereby giving a great deal of information away.

Methods

The paper presents a strategy for the analysis of recurrent data using a previously published study on the influence of different prosthetic treatment concepts for the shortened dental arch on tooth loss. A method based on cumulative sample history functions of recurrent events was adjusted for tooth loss. The shapes of these cumulative functions suggest a time dependency of the recurrence rate. To keep the model as simple as possible, a tripartite Poisson process (which assumes piecewise time-independent rates) was fitted to the cumulative mean functions stratified by treatment.

Results

Within the middle interval of the three-phasic process, the treatment effects differ significantly, which is interpreted as a delay of tooth loss due to the use of one type of prosthesis (fixed) compared with the other (removable).

Conclusions

An analysis based on cumulative history functions is based on process, therefore, temporally changing characteristics are better captured than in methods for survival analyses. The presented approach offers useful new insight into the temporal behavior of ongoing tooth loss after prosthetic treatment.

Trial registration

The trial has been registered at controlled-trials.com under ISRCTN97265367 (registration date 4 April 2008).

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-016-1360-y) contains supplementary material, which is available to authorized users.

An evolutionary stability perspective on oncogenesis control in mature T-cell populations

Here we present a mathematical model for the dynamics of oncogenesis control in mature T-cell populations within the blood and lymphatic system. T-cell homeostasis is maintained by clonal competition for trophic niches (survival signals stimulated through interactions with self-antigens bound to major histocompatibility molecules), where a clone is defined as the set of T cells carrying the same antigen specific T-cell receptor (TCR). We analytically derive fitness functions of healthy and leukemic clone variants, respectively, that capture the dependency of the stability of the healthy T-cell pool against leukemic invaders on clonal diversity and kinetic parameters. Similar to the stability of ecosystems with high biodiversity, leukemic mutants are suppressed within polyclonal T-cell populations, i.e., in the presence of a huge number of different TCRs. To the contrary, for a low clonal diversity the leukemic clone variants are able to invade the healthy T-cell pool. The model, therefore, describes the experimentally observed phenomenon that preleukemic clone variants prevail in quasi-monoclonal experimental settings (in mice), whereas in polyclonal settings the healthy TCR variants are able to suppress the outgrowth of tumours. Between the two extremal situations of mono- and polyclonality there exists a range of coexistence of healthy and oncogenic clone variants with moderate fitness (stability) each. A variation of cell cycle times considerably changes the dynamics within this coexistence region. Faster proliferating variants increase their chance to dominate. Finally, a simplified niche variation scheme illustrates a possible mechanism to increase clonal T-cell diversity given a small niche diversity.

Mice in an enriched environment learn more flexibly because of adult hippocampal neurogenesis

We here show that living in a stimulus‐rich environment (ENR) improves water maze learning with respect to specific key indicators that in previous loss‐of‐function experiments have been shown to rely on adult hippocampal neurogenesis. Analyzing the strategies employed by mice to locate the hidden platform in the water maze revealed that ENR facilitated task acquisition by increasing the probability to use effective search strategies. ENR also enhanced the animals’ behavioral flexibility, when the escape platform was moved to a new location. Treatment with temozolomide, which is known to reduce adult neurogenesis, abolished the effects of ENR on both acquisition and flexibility, while leaving other aspects of water maze learning untouched. These characteristic effects and interdependencies were not seen in parallel experiments with voluntary wheel running (RUN), a second pro‐neurogenic behavioral stimulus. Since the histological assessment of adult neurogenesis is by necessity an end‐point measure, the levels of neurogenesis over the course of the experiment can only be inferred and the present study focused on behavioral parameters as analytical endpoints. Although the correlation of physical activity with precursor cell proliferation and of learning and the survival of new neurons is well established, how the specific functional effects described here relate to dynamic changes in the stem cell niche remains to be addressed. Nevertheless, our findings support the hypothesis that adult neurogenesis is a critical mechanism underlying the beneficial effects of leading an active live, rich in experiences. © 2015 The Authors Hippocampus Published by Wiley Periodicals, Inc.

Computational modelling of embryonic stem-cell fate control

The maintenance of pluripotency in embryonic stem cells (ESCs), its loss during lineage specification or its re-induction to generate induced pluripotent stem cells are central topics in stem cell biology. To uncover the molecular basis and the design principles of pluripotency control, a multitude of experimental, but also an increasing number of computational, studies have been published. Here, we consider recent reports that apply computational or mathematical modelling approaches to describe the regulatory processes that underlie cell fate decisions in mouse ESCs. We summarise the principles, the strengths and potentials but also the limitations of different computational strategies.

Distinct roles of β-cell mass and function during type 1 diabetes onset and remission

Cure of type 1 diabetes (T1D) by immune intervention at disease onset depends on the restoration of insulin secretion by endogenous β-cells. However, little is known about the potential of β-cell mass and function to recover after autoimmune attack ablation. Using a longitudinal in vivo imaging approach, we show how functional status and mass of β-cells adapt in response to the onset and remission of T1D. We demonstrate that infiltration reduces β-cell mass prior to onset and, together with emerging hyperglycemia, affects β-cell function. After immune intervention, persisting hyperglycemia prevents functional recovery but promotes β-cell mass increase in mouse islets. When blood glucose levels return to normoglycemia β-cell mass expansion stops, and subsequently glucose tolerance recovers in combination with β-cell function. Similar to mouse islets, human islets exhibit cell exhaustion and recovery in response to transient hyperglycemia. However, the effect of hyperglycemia on human islet mass increase is minor and transient. Our data demonstrate a major role of functional exhaustion and recovery of β-cells during T1D onset and remission. Therefore, these findings support early intervention therapy for individuals with T1D.

Elucidating functional heterogeneity in hematopoietic progenitor cells: a combined experimental and modeling approach

A detailed understanding of the mechanisms maintaining the hierarchical balance of cell types in hematopoiesis will be important for the therapeutic manipulation of normal and leukemic cells. Mathematical modeling is expected to make an important contribution to this area, but the iterative development of increasingly accurate models will rely on repeated validation using experimental data of sufficient resolution to distinguish between alternative model scenarios. The multipotent hematopoietic progenitor FDCP-Mix cells maintain a hierarchy from self-renewal to post-mitotic differentiation in vitro and are accessible to detailed analysis. Here, we report the development of a combined mathematical modeling and experimental approach to study the principles underlying heterogeneity in FDCP-Mix cultures. We adapt a single-cell based model of hematopoiesis to the conditions of cell culture and describe an association between proliferative history and phenotype of FDCP-Mix cells. While data derived from population studies are incapable of distinguishing between three mechanistically different model scenarios, statistical analysis of single cell tracking data provides a resolution sufficient to select one of them. This scenario favors differences between granulocytic and monocytic lineage with respect to their proliferative behavior and death rates as a mechanistic explanation for the observed heterogeneity. Our results demonstrate the power of a combined experimental/modeling approach in which single cell fate analysis is the key to revealing regulatory principles at the cellular level.

Placement makes a difference: accuracy of an accelerometer in measuring step number and stair climbing

Accurate and easy-to-use measurement tools are required to evaluate the effect of treatments on patient activity. Comfortable device placement and fixation are important for patient compliance. The aim of this study was the evaluation of the accuracy of an accelerometer at different placements and slow velocities.

METHODS

A total of 43 healthy volunteers were included for a literature-based treadmill protocol using five accelerometer positions; a subset of 18 volunteers performed an extended treadmill protocol with velocities between 0.1 and 2.6m/s and finally stair climbing.

RESULTS

An alternative accelerometer position at the anterolateral aspect of the middle shank did measure steps more accurately than at the manufacturer suggested position, especially during slow velocities. Participants preferred the alternative placement at the shank. The accuracy of different accelerometer positions was excellent at velocities between 1.0 and 2.2m/s. During slow velocities below 1.0m/s steps were recorded less accurately. Accepting an error of five percent, the accelerometer recorded steps accurately from 0.5m/s at the alternative placement and from 0.8m/s at the manufacturer suggested placement. Stair climbing was not recorded accurately by any accelerometer position.

CONCLUSION

For measuring step number during slow velocities, the alternative position should be favoured. Stair climbing was not recorded accurately by any tested placement.

A model-based analysis of culture-dependent phenotypes of mESCs

Mouse embryonic stem cells (mESCs) can be maintained in a proliferative and undifferentiated state over many passages (self-renewal) while retaining the potential to give rise to every cell type of the organism (pluripotency). Autocrine FGF4/Erk signalling has been identified as a major stimulus for fate decisions and lineage commitment in these cells. Recent findings on serum-free culture conditions with specific inhibitors (known as 2i) demonstrate that the inhibition of this pathway reduces transcription factor heterogeneity and is vital to maintain ground state pluripotency of mESCs. We suggest a novel mathematical model to explicitly integrate FGF4/Erk signalling into an interaction network of key pluripotency factors (namely Oct4, Sox2, Nanog and Rex1). The envisaged model allows to explore whether and how proposed mechanisms and feedback regulations can account for different expression patterns in mESC cultures. We demonstrate that an FGF4/Erk-mediated negative feedback is sufficient to induce molecular heterogeneity with respect to Nanog and Rex1 expression and thus critically regulates the propensity for differentiation and the loss of pluripotency. Furthermore, we compare simulation results on the transcription factor dynamics in different self-renewing states and during differentiation with experimental data on a Rex1GFPd2 reporter cell line using flow cytometry and qRT-PCR measurements. Concluding from our results we argue that interaction between FGF4/Erk signalling and Nanog expression qualifies as a key mechanism to manipulate mESC pluripotency. In particular, we infer that ground state pluripotency under 2i is achieved by shifting stable expression pattern of Nanog from a bistable into a monostable regulation impeding stochastic state transitions. Furthermore, we derive testable predictions on altering the degree of Nanog heterogeneity and on the frequency of state transitions in LIF/serum conditions to challenge our model assumptions.

High-speed panoramic light-sheet microscopy reveals global endodermal cell dynamics

The ever-increasing speed and resolution of modern microscopes make the storage and post-processing of images challenging and prevent thorough statistical analyses in developmental biology. Here, instead of deploying massive storage and computing power, we exploit the spherical geometry of zebrafish embryos by computing a radial maximum intensity projection in real time with a 240-fold reduction in data rate. In our four-lens selective plane illumination microscope (SPIM) setup the development of multiple embryos is recorded in parallel and a map of all labelled cells is obtained for each embryo in <10 s. In these panoramic projections, cell segmentation and flow analysis reveal characteristic migration patterns and global tissue remodelling in the early endoderm. Merging data from many samples uncover stereotypic patterns that are fundamental to endoderm development in every embryo. We demonstrate that processing and compressing raw image data in real time is not only efficient but indispensable for image-based systems biology.

Systematic large-scale analysis of embryonic development requires the processing of large amounts of microscopy data. Here Schmid et al. solve this problem by developing a high-speed imaging system that projects zebrafish embryos onto a ‘world map’ in real time, revealing characteristic migration patterns in the early endoderm.

Multiplexing clonality: combining RGB marking and genetic barcoding

RGB marking and DNA barcoding are two cutting-edge technologies in the field of clonal cell marking. To combine the virtues of both approaches, we equipped LeGO vectors encoding red, green or blue fluorescent proteins with complex DNA barcodes carrying color-specific signatures. For these vectors, we generated highly complex plasmid libraries that were used for the production of barcoded lentiviral vector particles. In proof-of-principle experiments, we used barcoded vectors for RGB marking of cell lines and primary murine hepatocytes. We applied single-cell polymerase chain reaction to decipher barcode signatures of individual RGB-marked cells expressing defined color hues. This enabled us to prove clonal identity of cells with one and the same RGB color. Also, we made use of barcoded vectors to investigate clonal development of leukemia induced by ectopic oncogene expression in murine hematopoietic cells. In conclusion, by combining RGB marking and DNA barcoding, we have established a novel technique for the unambiguous genetic marking of individual cells in the context of normal regeneration as well as malignant outgrowth. Moreover, the introduction of color-specific signatures in barcodes will facilitate studies on the impact of different variables (e.g. vector type, transgenes, culture conditions) in the context of competitive repopulation studies.