Interest in the role of Beta-secretase1 (BACE1) in Alzheimer’s disease (AD) pathogenesis and pathophysiology has been remarkably growing in the last 10 years [The burst of published literature on the topic mainly stems from the potential significance of this key-enzyme of amyloidogenic pathway as pharmacological target . Based oThe above findings can be discussed as follow:1. The increase in BACE1 serum activity in MCI patients suggests that this enzyme is more active before the onset of overt dementia. Moreover, this abnormality appears to occur irrespectively of the possible clinical course of these patients (since it is observed in na- and a-MCI). Previous post-mortem and CSF findings on MCI indicate that the precocious increase in peripheral BACE1 might reflect CNS conditions . ConsistThese findings are not surprising, since it is well-known that Aβ deposition may precede AD clinical symptoms by approximately 15-20 years. It is also well-established that synapse loss and dysfunction is a key feature in many neurodegenerative diseases including dementia and, in particular, AD . BACE1 h2. The selective increase of serum BACE1 activity in converter aMCI with a higher MMSE might suggest that this could be a risk factor for dementia in those patients where the cognitive decline has not overtly taken place. It is tempting to speculate, that the contribution of BACE1 in the clinical trajectory to dementia is tangible only when other pathogenic players have not yet become predominant. Indeed, while it is widely recognized that Aβ formation is the initial event in AD, it has also become increasingly apparent that secondary abnormalities such as tau and neuroinflammation might play a major role in the subsequent neurodegeneration . In otheIn summary, BACE1 up-regulation might be one of the first abnormal event in AD (and VD). Within this long preclinical phase of the disease, which lasts years before the diagnosis of MCI state, BACE1 may be the key-player of the initial, and still asymptomatic, neuropathological changes. In this time frame, the evaluation of its serum activity might have clinical relevance as early biomarker, mostly for screening subjects to enroll in clinical trials. The time when Aβ accumulation is the prominent alteration, and the specific weight of other pathogenic actors is still relatively low, BACE1 could also be candidate therapeutical target. As recently suggested by Panza et al. , large r


Evidence-based infectious disease and intensive care management is more relevant than ever. Medical expertise in the two disciplines is often geographically limited to university institutions. In addition, the interconnection between inpatient and outpatient care is often insufficient .This study aims to establish and evaluate a telemedical inpatient-outpatient network based on expert teleconsultations to increase treatment quality in intensive care medicine and infectious diseases.Choosing Wisely recommendations for infectious disease management. Guideline adherence was analyzed using binary logistic regression models.We performed a multicenter, stepped-wedge cluster randomized trial (February 2017 to January 2020) to establish a telemedicine inpatient-outpatient network among university hospitals, hospitals, and outpatient physicians in North Rhine-Westphalia, Germany. Patients aged ≥18 years in the intensive care unit or consulting with a physician in the outpatient setting were eligible. We provided expert knowledge from intensivists and infectious disease specialists through advanced training courses and expert teleconsultations with 24/7/365 availability on demand respectively once per week to enhance treatment quality. The primary outcome was adherence to the 10 Staphylococcus aureus infections  and in sepsis management in critically ill patients . There was a statistically nonsignificant decrease in sepsis-related mortality from 29% (19/66) in the control group to 23.8% (50/210) in the intervention group. Furthermore, the extension of treatment with prophylactic antibiotics after surgery was significantly less likely . Patients treated by outpatient physicians, who were regularly participating in expert teleconsultations, were also more likely to be treated according to guideline recommendations regarding antibiotic therapy for uncomplicated upper respiratory tract infections  and asymptomatic bacteriuria . For the other recommendations, we found no significant effects, or we had too few observations to generate models. The key limitations of our study include selection effects due to the applied on-site triage of patients as well as the limited possibilities to control for secular effects.Overall, 159,424 patients  from 17 hospitals and 103 outpatient physicians were included. There was a significant increase in guideline adherence in the management of Telemedicine facilitates a direct round-the-clock interaction over broad distances between intensivists or infectious disease experts and physicians who care for patients in hospitals without ready access to these experts. Expert teleconsultations increase guideline adherence and treatment quality in infectious disease and intensive care management, creating added value for critically ill patients.ClinicalTrials.gov NCT03137589; https://clinicaltrials.gov/ct2/show/NCT03137589 Worldwide, health workforce shortages are a pressing concern. By 2030, it is estimated that there will be a shortage of 9.9 million physicians and nurses worldwide ,2. In adIncreasing antimicrobial resistance (AMR) poses a growing threat to patients. Important underlying drivers are the overuse and misuse of antibiotics. Every year, approximately 700,000 patients worldwide die from infections that are treatable with antibiotics . Global In 2017, there were approximately 48.9 million cases of sepsis worldwide and 11 million sepsis-related deaths  . Sepsis Choosing Wisely initiative, have responded to this global challenge by increasing professional awareness of evidence-based medicine ““In critically ill patients with signs of infection, early appropriate antibiotic therapy is crucial after obtaining cultures, and treatment should be regularly re-evaluated” [P2]“Prescribe oral forms of highly bioavailable antimicrobial agents to patients who can reliably receive and absorb medications via the enteral route” [P5]“Do not treat asymptomatic bacteriuria with antibiotics” [N2]“Do not treat Candida recovered from respiratory or gastrointestinal tract specimens” [N3]“Do not extend the administration of prophylactic antibiotics after surgery (after the patient has left the operating room)” [N4]“Do not treat elevated C‐reactive protein or procalcitonin levels in serum with antibiotics in patients not presenting signs or symptoms of infection” [N5]Choosing Wisely recommendationsPrimary outcome outpatient sector: adherence to the following “Annual influenza vaccination should be given to individuals aged >60 years, patients with specific comorbidities, and people  who may infect vulnerable persons” [P3]“All children should receive the measles vaccine, and adults born after 1970 without prior documented vaccination against measles should get at least one dose of the vaccine” [P4]“Avoid prescribing antibiotics for uncomplicated upper respiratory tract infections including bronchitis” [N1]“Do not treat asymptomatic bacteriuria with antibiotics” [N2]Participating hospitals and outpatient physician offices were randomly assigned to 4 clusters of 4 to 5 hospitals and 4 clusters of 23 to 28 outpatient physicians’ offices  and 2. PPrimary outcomes were evaluated using binary logistic regression models (primary data) and zero-or-one inflated beta regression models (AHIP data).In the analyses of the inpatient data, we controlled for the treating hospital, patient age, and the Sequential Organ Failure Assessment (SOFA) score at enrollment in the study . SOFA scTo differentiate the training effect and the external effect of expert teleconsultations from the direct counseling effect, the intervention group was separated into patients with and without expert teleconsultations, as not all patients who received interventions were treated with expert teleconsultations. Models for ICU and sepsis-related mortality and sepsis bundle compliance were specified in the same manner. Effects on the LOS were estimated using linear, gamma, and log-linear regression models with the same control variables.In the evaluation of the primary outpatient data, we controlled for the treating outpatient physician and patient age. Three different models were estimated for each outcome:Model 1 contained only the group variable (group) and the control variables.In addition, model 2 contained a count variable (n) that recorded the number of expert teleconsultations the outpatient physician had already used before the visit with the respective patient.Finally, model 3 contained a quadratic term of the count variable (n2) to map possible learning curves among outpatient physicians in the sense of a decreasing marginal utility of the expert teleconsultations.In the AHIP data, influenza and measles vaccination rates were documented quarterly at the practitioner level. To isolate the effect of the intervention on the vaccination rates, we controlled for the treating practitioner, the number of patients during the quarter and seasonal or quarterly effects.t tests for metric variables. Data cleaning and statistical analyses were performed in R  using the functions glm for logistic models and gamlss for beta regression models. CIs for regression estimates were computed using the confint command from the stats package, which calculated the CIs based on profile likelihood estimation. All analyses were based on a significance level of α=.05. The model structures are detailed in the Baseline group differences were tested using odds ratios (ORs) for binary variables or 2-tailed A total of 17 hospitals and 103 outpatient physicians underwent randomization. The participating hospitals had between 101 and 449 beds; participating ICUs had on average 10 ICU beds (range 5-14) and were mixed medical-surgical ICUs staffed with anesthesiologists and internists. Hospitals were in both urban and rural areas. Of the 17 hospitals, 8 (47%) served populations <50,000; 7 (41%) served populations from 50,000 to 100,000; and 2 (12%) served populations >250,000. Most outpatient physicians were in urban areas with a high outpatient physician density per capita. Among the participating doctors, multiple specialties were represented, and the majority were general practitioners, internists, ophthalmologists, or gynecologists. Between May 3, 2017, and September 30, 2019, we enrolled patients  who required infectious disease or intensive care treatment in our study . OverallStaphylococcus aureusbloodstream infection imperatively needs efficacious antimicrobial treatment and identification and elimination of the source of infection, the SOFA score was not included in the statistical model, as we mainly observed non-ICU patients for this outcome, for whom the relevant parameters to calculate the score are not routinely recorded.After initial triage by the attending physicians, we enrolled 10,585 inpatients . The basCompared with those in the control group, inpatients in the intervention group were older (mean age 69.25 years in the control group vs 72.14 years in the intervention group) and had higher SOFA scores at baseline (mean SOFA 3.58 in the control group vs 4.12 in the intervention group). The higher morbidity in the intervention group also manifested itself in a higher sepsis rate (5% vs 9%) and a higher ARDS rate (13% vs 17.9%). For the outcome-specific analysis samples, we found no fundamental deviations from the characteristics of the overall sample.Staphylococcus aureus bloodstream infections . As expert teleconsultations were provided to all patients in this analysis, we did not divide the intervention group to estimate the effects of the intervention on this recommendation. Patients in the intervention group were significantly more likely to be treated in accordance with the recommendation  and Do not treat Candida recovered from respiratory or gastrointestinal tract specimens (n=32), we had too few observations to generate logistic regression models . This was mainly driven by an improvement in compliance with the 4 to 6-hour bundle. With expert teleconsultations, the odds of being treated in accordance with the guideline recommendations in the 4- to 6-hour therapy course after receiving a diagnosis of sepsis were 14.2 times higher  than in the control group. Overall and for the 3- and 6-hour sepsis bundles, we found significant improvements in compliance for patients in the intervention group who did not receive direct telemedical treatment support.Significant between-group differences were also found in the adherence to the 3- and 6-hour sepsis bundles for patients with the diagnoses of severe sepsis and septic shock . OverallP=.02) was obtained for patients without expert teleconsultations. Patients with expert teleconsultations also showed a significantly increased chance of correct treatment mild ARDS . For patients with moderate or severe ARDS, we could not demonstrate these effects. A detailed description of this outcome is presented in Regarding treatment quality in patients with mild ARDS, the descriptive analysis showed an increase in compliance from 7.4% (16/217) in the control group to 18.4% (9/49) in the intervention group without expert teleconsultations and 11.8% (19/161) in the group with expert teleconsultations. In the logistic regression model, a significant OR of 3.621  and sepsis-related mortality , no statistically significant intervention effects were found in our model estimations; however, a reduction in the sepsis-related mortality rate of 5% was achieved . Also, no significant improvements with regard to hospital mortality were observed.Regarding ICU mortality  and without  expert teleconsultations than for the respective controls. Regarding hospital LOS, it is noticeable that patients in the control group were hospitalized longer  than patients in the intervention group without expert teleconsultations  but for shorter periods than patients with expert teleconsultations . The linear regression model confirms this result for patients with expert teleconsultations. These patients stayed on average 4.6 days  longer in hospital than control patients. For the group of patients without expert teleconsultations, there was no significant difference to the control group.ICU LOS was significantly longer for intervention patients with  of the patients in the control group and 1.1% (8/739) in the intervention group were discharged from hospital on dialysis. With this very low prevalence, effects of any intervention cannot be shown.P<.001). In patients in the intervention group without expert teleconsultations, this chance was also significantly increased compared with that in the control group . Overall, the analyses thus show an increase in the number of transfers owing to the intervention.Overall, 4.38% (86/1965) of the patients in the control group were transferred to another hospital during our study. In the intervention group, this proportion was 11.34% (143/1261). Patients with expert teleconsultations were transferred more frequently  than patients in the intervention group without expert teleconsultations . The model calculation also shows a significant intervention effect. Patients who received an expert teleconsultation had a 2.9-fold higher chance of being transferred . This effect was significantly influenced by the number of expert teleconsultations conducted, as indicated by the estimated coefficient of the count variable . In addition, the estimated coefficient for the quadratic term of the count variable showed a statistically significant negative effect, which illustrates the decreasing marginal utility of the expert teleconsultations . Here, we also observed an accumulation of noncompliance in the treatment of older patients . This effect was influenced more by the number of expert teleconsultations conducted than by the training of the treating physicians . Although not statistically significant, we also observed a trend toward a decreasing marginal utility of the expert teleconsultations with regard to this outcome (model 3). The Choosing Wisely recommendations also provide advice for increasing influenza (P3) and measles vaccinations (P4). For these analyses, we examined the quarterly vaccination rates at the physician level during the study period. In our basic model, we found no significant effect of expert teleconsultations on influenza vaccinations . However, to better capture seasonal effects on the vaccination rates, we additionally constructed a model in which we extended the observation period and considered the transition phase as part of the intervention phase because the expert training occurred at the end of the control phase. In this model, intervention physicians had significantly higher influenza vaccination rates . Regarding measles vaccination rates, we found no significant intervention effects in either model.Our telemedical inpatient-outpatient network also achieved significant results with regard to the management of asymptomatic bacteriuria (N2). Patients in the intervention group were more likely to be treated in line with the guideline recommendations than were the controls . This corresponds to 0.4%  of the patients included in the outpatient study. Of the 540 patients, 72 (13.3%) were eligible for a further survey after 3 or 12 months, as the contact data required for a renewed contact were collected for them at t0. The response rates in the follow-ups for these 72 patients were 32% (n=23) for t1 and 28% (n=20) for t2. An analysis of changes over time did not appear to be appropriate based on the low response rates for t1 and t2, and no analysis was performed.To the best of our knowledge, TELnet@NRW is the largest telemedical cluster randomized controlled study in Europe, with more than 150,000 patients. We established a telemedical inpatient-outpatient network as a novel digital structure in the health care system, and we found that it measurably improved the quality of patient care. The consistent introduction and implementation of standardized communication using a certified electronic patient record was a feature that was essential for increasing the effectiveness of the processes involving the new digital health network. The key findings of this study suggest that expert teleconsultation is an effective tool to provide inpatient and outpatient physicians with evidence-based expertise on a large scale, thus improving guideline compliance and the quality of infectious disease and intensive care management.Staphylococcus aureus bloodstream infections (P1), severe sepsis and septic shock (P2), and prophylactic antibiotic therapy (N4). Quality improvements for several outcomes reached not only those patients who were treated with direct expert teleconsultations but also other patients treated by the same physicians. This finding can be interpreted as a positive effect of the initial training courses and/or an indirect effect of the expert teleconsultations. It can therefore be assumed that the treating physicians also apply the knowledge acquired in teleconsultations to patients whose treatment is carried out without telemedical support.Concerning the inpatient primary outcomes, our telemedical intervention had significant quality-improving effects on the management of In the outpatient sector, our telemedical intervention significantly increased guideline compliance in the management of uncomplicated upper respiratory tract infections (N1) and asymptomatic bacteriuria (N2). The chance of being treated according to the recommendations was positively associated with the number of teleconsultations already participated in by the outpatient physician before the respective patient visit. Furthermore, we found evidence for a decreasing marginal utility of these teleconsultations, which may reflect the physicians’ learning curve. To obtain a better understanding of physicians’ learning behavior and the associated practical implications, studies with longer observational periods are needed. Basic analyses of influenza and measles vaccination rates uncovered no significant intervention effects. However, when considering the initial transition phase as part of the intervention phase, influenza vaccination in the intervention group increased significantly. This could be explained either by the better statistical control of seasonal effects (as more observation quarters were included) or by the fact that the effects were larger immediately after the start of the intervention. The latter would indicate that the transition period was probably too long to capture the full potential of our intervention. Nevertheless, as this analysis deviates from the original study plan, it can only be interpreted as exploratory.With regard to our secondary outcomes analyzed for the inpatient sector, we found that the provision of expert teleconsultations led to a higher overall sepsis bundle adherence, which was mainly driven by improvements in compliance with the 4- to 6-hour bundle. However, although the direct relationship between sepsis bundle compliance as a quality-of-care indicator and mortality is well documented in the scientific literature -15,20,29Choosing Wisely recommendations as the primary outcome of this study, parts of these recommendations are not applicable to both inpatient and outpatient care, especially the ICU setting. Because gastrointestinal function is very often impaired in patients in the ICU and therefore the absorption of oral medications cannot be guaranteed, most medication is administered intravenously. Likewise, obtaining tracheal secretions and microbiological tests is a rarity in the outpatient sector, as the therapeutic consequence outside of serious infections is very low. As most patients in the ICU have an indwelling urinary catheter and are sedated, the criterion asymptomatic cannot be evaluated in the context of bacteriuria. Notably, the compliance with abstaining from treating elevated C‐reactive protein or procalcitonin levels in the serum with antibiotics in patients without signs or symptoms of infection was already 90% (531/590) in the control phase. In summary, 40% (4/10) of Choosing Wisely recommendations for infectious disease management are not fully applicable to ICU care.Although its cross-sectoral applicability was an argument for selecting compliance with the Staphylococcus aureus bacteremia. Guideline adherence significantly improves clinical outcomes and reduces mortality [Within the real-world setting of our trial, it was at the discretion of the practitioners for which patients an expert teleconsultation was requested. Hence, attending physicians tended to include patients thought to be more ill during the intervention phase of the study, which is reflected by the higher SOFA scores, sepsis, and ARDS incidences for inpatients in the intervention phase compared with those in the control phase. We controlled for such selection effects by including relevant variables in our regression models, but we could not rule out the presence of unknown confounders for which there were no data available. Although our regression models were adjusted for the SOFA score, this may not have fully controlled for the differences in the baseline risks of morbidity and mortality between the study groups. It is unclear to what extent this problem has been aggravated by the frequent occurrence of missing values for the different SOFA subscores and the associated need for data imputation. It should be highlighted that TELnet@NRW was not designed or powered to detect differences in sepsis-related mortality because the primary outcome focused on quality indicators for infectious disease management . Neverthortality -35.As Hemming et al  note, coOverall, the ﬁndings of this study should be interpreted in the context of its limitations. The participating hospitals, ICUs, and outpatient physicians were not chosen at random; instead, the 17 sites and 103 outpatient physicians were self-selected based on their willingness to participate in a study to improve patient care. However, we chose a randomized stepped-wedge design to control for clinical characteristics, demographics, and setting to protect our findings against bias. Nevertheless, our results should be interpreted with the consideration of the possibility for selection bias due to the on-site triage of patients. We controlled for such selection effects by including relevant variables in our regression models, but we could not rule out the presence of unknown confounders for which there were no data available. We also acknowledge potential bias related to secular trends in care given the fact that the control and intervention clusters did not overlap in time. Furthermore, for some outcomes, the real effect size remained uncertain, which is reflected by the large CI. This holds true especially for sepsis bundle compliance. Nevertheless, there is sufficient certainty that the associated ORs were >1; thus, intervention effects existed.Despite the mentioned limitations, TELnet@NRW robustly demonstrated that a cross-sectoral health network, as a new digital structure in the health care system, can develop into a quality network that operates under the premise that cross-sectoral and interregional cooperation can significantly improve evidence-based care. On the basis of the technical equipment, the principles of TELnet@NRW are transferable from intensive care medicine and infectious disease management to other subspecialist medical fields that mostly rely on expert knowledge. Thus, the concept of TELnet@NRW can be adapted to other patient populations, other conditions, or other areas, in which expertise rather than equipment needs to be transported over large distances. Our results must also be interpreted in light of the most recent SARS-CoV-2 pandemic; a digital inpatient-outpatient health network is well suited to meeting pressing challenges faced by health care systems, which we will have to address in the future . However, further research is needed with regard to the long-term patient-relevant effects of our telemedical solution and its cost-effectiveness especially in less complex cases in the outpatient setting.


In the absence of such reagents, the power of image-based classification has remained mostly off-limits to many research organisms. We have developed an image-based classification methodology we named Image3C (Image-Cytometry Cell Classification) that does not require species-specific reagents nor pre-existing knowledge about the sample. Image3C combines image-based flow cytometry with an unbiased, high-throughput cell clustering pipeline and CNN integration. Image3C exploits intrinsic cellular features and non-species-specific dyes to perform de novo cell composition analysis and detect changes between different conditions. Therefore, Image3C expands the use of image-based analyses of cell population composition to research organisms in which detailed cellular phenotypes are unknown or for which species-specific reagents are not available.Image-based cell classification has become a common tool to identify phenotypic changes in cell populations. However, this methodology is limited to organisms possessing well-characterized species-specific reagents ( i.e., how many cells and which types of cells are present inside an organ can indicate what that organ does. It can also reveal how that organ changes under different conditions, like during an infection or treatment.Cells are the building blocks of all living organisms. They come in many types, each with a different role. Understanding the composition of cells, The most powerful methods for studying cells work well for species researchers already know a lot about, such as mice, zebrafish or humans, but not for less studied animals. To change this Accorsi, Box, Peuß et al. created a new tool called Image3C to be used for studying the composition of cells in less researched organisms.Instead of using reagents that only work for specific species, the tool uses molecules that work across many species, like dyes that stain the cell nucleus. A cell-sorting machine, known as a flow cytometer, connected to a microscope then takes pictures of hundreds of stained cells each second and Image3C groups them based on their appearance, without the need for any prior knowledge about the cell types. Accorsi et al. then tested Image3C on immune system cells of zebrafish, a well-studied animal, and apple snails, an under-studied animal. For both species, the tool was able to sort cells into groups representing different parts of the immune system.Image3C speeds up the grouping process and reduces the need for user intervention and time. This lowers the risk of bias compared to manual counting of cells. It can sort cells even when the types of cells in an organism are unknown and even when specialized reagents for an organism do not exist. This means that it could characterise the cell make-up of new tissues coming from organisms never studied before. Access to this uncharted world of cells stands to reveal previously inaccessible clues about how organs behave and evolve and allow researchers to investigate the impact of environmental changes on these cells. However, the same cannot be said for a growing number of important, yet understudied research organisms lacking such reagents and whose biological interrogation would benefit immensely from single-cell analyses. In these cases, classical histochemical methods are often used to identify and characterize specific cells. Yet, the successful identification and enumeration of biologically meaningful cell types in such studies can be harmed by both the limited number and variety of cellular attributes (few features or low dynamic range) available for determination of cell types and by observer bias when using traditional, hand-counting approaches   . These se.g., cell-specific morphological traits within an image set) or the availability of cell-specific reagents , or genomic sequence   and/or availability of antibodies or molecular databases. For example, within the interdisciplinary field of eco-immunology, a growing number of researchers are investigating immune system adaptation to different environments by studying immune cell compositions in diverse animals  has become a promising approach for accurate high-throughput cell analysis that is free from observer bias . To dateuencing) . This me animals . Given t animals , applyin animals .e.g., nuclear staining or dyes for metabolic states that function well in a variety of organisms) . This method analyzes, visualizes, and quantifies, in a high-throughput and unbiased way, the composition of cell populations by using cell morphological traits and non-species-specific fluorescent probes (ganisms) , Table 1fication , Table 1staining , pre-existaining , and is staining  for a prImage3C is an imaging tool developed to study tissue composition at single-cell resolution in research organisms for which antibodies and pre-existing knowledge about cell types are not readily available , Table 1X Mark II . We validated reagents experimentally by determining that nuclear dyes stain intracellular material matching expected characteristics of nuclear DNA or by activation of cells with drugs to change their metabolic state. The labeled samples are then run on the ImageStream Mark II . ImageSt Mark II , and, ifOnce images of individual events are collected for each channel of interest, feature values from both morphological and fluorescent data, such as cell size and nuclear size, are extracted from the cell images using IDEAS software   and 2. Ti.e., the mean value of each feature for each cluster)  graphs in the VorteX clustering environment  free to. Cell imcluster)  allow toe.g., homeostatic state) and the cell clusters are defined for the tissue of interest, the images and the relative clustering IDs can be used to train a CNN classifier in an unbiased way  to set up a classifier in an unbiased way for later use as a reference in analyzing the effects of experimental manipulations on these cell populations. We conclude from these results that Image3C can perform de novo high-throughput characterizations of population composition and define specific cell type changes between homeostatic and experimentally perturbed samples across multiple experiments.Once this pipeline is run on a first set of samples  in homeostasis condition, stained them, and ran on the ImageStreamX Mark II. We analyzed intrinsic morphological and fluorescent features, such as cell and nuclear size, shape, and darkfield signal (side scatter [SSC]). Feature values were extracted from each cell image and processed through our pipeline  that mainly differ in cell diameter -relative abundance, an important tool for comparing population composition across different treatment groups under different environmental conditions . Here, wNext, we sought to determine whether Image3C could be used to characterize and quantify biological processes by identifying a tissue of interest and then comparing cellular composition dynamics, functions, and physiological responses of specific cell types across a range of experimental conditions. Our goal was to detect statistically significant changes in cluster relative abundance between control and treated samples to gain a more detailed understanding of cell population dynamics and individual cell function.Staphylococcus aureus (CTV-S. aureus) and with dihydrorhodamine-123 (DHR), a ROS indicator that becomes fluorescent if oxidized to report oxidative bursting following phagocytosis. As controls, we inhibited phagocytosis through cytoskeletal impairment with cytochalasin B (CCB) incubation or through incubation with bacteria at lowered temperature by placing culture plates on ice. Events collected on the ImageStreamX Mark II were analyzed with Image3C and clustered in 26 distinct clusters using quantifications of morphological and fluorescent features  and induce a ROS response  (S. aureus and the samples where phagocytosis is inhibited (CTV-S. aureus + CCB and CTV-S. aureus + Ice), we used the statistical analysis included in Image3C based on a negative binomial regression model  that actively phagocytize CTV-S. aureus bacteria  . In zebr signal) . To compon model  and 6. Son model  while coon model  allowed r Dr21_P . The mortrophils  that bectrophils . Overalltrophils , which itrophils . It is atrophils  we did nbacteria . CompareS. aureus samples compared to the CTV-S. aureus + CCB ones (S. aureus) were significantly more abundant in the CTV-S. aureus + CCB sample  and compared the specificity of inhibition with the CTV-S. aureus + CCB sample (Dr12_P)  . This isPomacea canaliculata, an emerging organism for which molecular and cell biological tools have yet to be fully developed. As such, we repeated the same experiments done in zebrafish on the hemolymph of P. canaliculata. For morphological examination of the cellular composition of the hemolymph collected from female adults in homeostasis conditions, we stained the single cell suspensions with Draq5 (DNA dye) and ran on the ImageStreamX Mark II. We used Image3C to analyze the images of the events and identified 9 cell clusters (Pc5 and Pc8) and the other clusters, based on inspection of cell images, were grouped into two main categories (Pc4) and intermediate cells (clusters Pc2 and Pc3) with high nuclear-cytoplasmic (N/C) ratio. These cells morphologically resemble the Group I hemocytes previously described using a classical morphological approach . Likely, these cells correspond to the previously described Group II hemocytes that include both granular and agranular cells  amongst all clusters other than cell doublets .To identify which of these clusters were enriched for granular cells, we looked at the heatmap with feature values for each individual cluster , which correspond to 7.7% of all hemocytes by classical histological methods and 8.9% by Image3C. The intermediate cells (clusters Pc2 and Pc3) are the least represented in both approaches, with a relative abundance of 5.6% and 10.6% for the manually and Image3C analyzed events, respectively  or using low temperature by incubating samples on ice (CTV-S. aureus + Ice). Events collected on the ImageStreamX Mark II were analyzed with Image3C and clustered in 20 distinct clusters using quantifications of morphological and fluorescent features  with samples where phagocytosis was inhibited by EDTA incubation or low temperature using the statistical analysis included in Image3C based on a negative binomial regression model (Pc5_P and Pc17_P)   . The two signal)  and 11. d sample , showings sample . We can The data analysis with Image3C clearly highlighted that CCB and EDTA, two classical phagocytic inhibitors commonly used in controls for phagocytosis experiments in vertebrates and invertebrates, respectively, result in a drastic change of cell morphology and cell viability. This consequence is not easily detectable by other methods and is therefore often overlooked. In the present work, these changes significantly modified the overall cell cluster number and distribution and indicate that the effects of CCB and EDTA on cell morphology should be taken into consideration in any study of morphological features of cells with phagocytosis properties because artifacts might be significant.e.g., homeostasis condition) .When determining differences between control and experimental treatments, Image3C necessarily combines images and data from all samples and then clusters the cells. This must be taken into consideration for experimental planning. Experiments meant to analyze cell composition and morphological diversity in one biological domain (ndition)  should bThis last point is probably the most challenging since mistakes can easily be introduced based on user biases or lack of sufficient pre-existing knowledge about cell morphologies or of cell biomarkers that would allow a confident cross-annotation between multiple FDL graphs. In addition, we observed that the number of clusters drastically increases when including treatments that influence cell morphological properties of the cell. As an example, while we detected 9 unique clusters in naïve hemolymph samples, we detected 20 clusters in the phagocytosis experiment . This isde novo clustering and cell type identification in homeostasis samples, we included in Image3C the possibility to use these initial images and their cluster IDs to train a CNN without manually classifying the images  analyzed in an unbiased way by de novo clustering and (2) assign new cell images acquired through ImageStreamX Mark II system to their respective classes. As proof of concept, we used the clusters identified for P. canaliculata hemocytes in homeostasis condition with the first part of the pipeline  to increase accuracy of the classifier . We found that 59.4, 6.2, and 9.2% of the phagocytes belonged to cluster Pc1_CT, Pc6_CT, and Pc7_CT, respectively  in invertebrate hemocytes that resembles the nodule formation observed in vivo (de novo clustering of hemocytes in homeostasis condition) without the need to reannotate the FDL obtained during the phagocytosis assay  and Pc6_CT (Group II granular large hemocytes)  while the other agranular (Pc7_CT) as well as the granular large hemocytes (Pc6_CT) was significantly more abundant in the male animals   . Signifimocytes) . The redctively) .While the biological significance of this observation is not going to be further investigated in this paper, the discovery highlights the power of Image3C analysis compared to traditional methods for determining and quantifying the composition of cell populations. These experiments demonstrate that Image3C, in combination with the presented convolutional classifier, can analyze large experimental datasets and identify significances with small effect sizes. Importantly, Image3C analysis is independent of observer biases and does not require prior knowledge about expected tissue composition or the expected effect of treatment on cell morphology.de novo clustering and highlight important changes in cell type abundance and cell population composition caused by experimental or natural perturbation . Image3C does not require, at any point, prior knowledge about the tissue composition or cell type-specific markers, although, if available, they can be included and used. Furthermore, in combination with the CNN classifier trained on these clusters, we demonstrate that Image3C is capable of bias-free and high-throughput analysis of large experimental datasets making it possible to compare a specific cell type behavior or population composition across multiple experiments. Image3C is extremely versatile and can be applied to any tissue or cell population of interest and is adaptable to a variety of experimental designs. Although Image3C was developed in response to the need of analyzing cell composition of tissues in emerging research organisms, the Image3C tool could be potentially used also to add to transcriptomic dataset an additional and complementary layer of information based on cell morphology. Given the recent advancement in image-based flow cytometry that enables image capturing together with cell sorting , biomarkers, single-cell atlases, or a high-quality genome for a scRNA-seq approach are not available. We demonstrated that Image3C can identify different cell populations based on morphology and/or function through  sorting , a scRNATwelve-month-old, wild type, female, adult zebrafish were euthanized with cold 500 mg/L MS-222 solution for 5 min. WKM was dissected as previously described  and tranP. canaliculata  were maintained and bred in captivity, in a water recirculation system filled with artificial freshwater . The snails were fed twice a week and kept in a 10:14 light:dark cycle. Wild-type adult snails, 7–9 months old and with a shell size of 45–60 mm, were starved for 5 days before the hemolymph collection  is constituted by 22% (v/v) Schneider’s Drosophila Medium, 4.5 g/L lactalbumin hydrolysate, 1.3 g/L galactose, 0.02 g/L gentamycin in MilliQ water, pH 7.0.Specimens of the apple snail llection . If not 5 cells/well in a 96-well plate in 200 µL of L-90 media and incubated for 3 h at room temperature. Cells were stained with 5 µM Draq5 (Thermo Fisher Scientific) for 10 min and subsequently run on the ImageStreamX Mark II (Amnis Millipore Sigma), where 10,000 nucleated and focused events were recorded for each sample (n = 8). Erythrocytes were outgated to enrich for immune-relevant cells and prevent overclustering in the subsequent analysis. The latter is due to the fact that fish erythrocytes are nucleated and their biconcave shape results in different morphological feature values only depending on their orientation during image acquisition.WKM cells from zebrafish were isolated as described before and plated at 4 × 10P. canaliculata hemocytes were stained with 5 µM Draq5 for 10 min, moved to ice, and subsequently run on the ImageStreamX Mark II, where 10,000 nucleated and focused events were imaged for each sample (n = 5).The Staphylococcus aureus (Thermo Fisher Scientific) were resuspended in PBS at the final concentration of 100 mg/mL and incubated with 5 µM CTV (Thermo Fisher Scientific) for 20 min. Labeled bacteria were centrifuged and resuspended in PBS for three times to remove unbound dye and then stored at −20 °C as single-use aliquots. Cells, obtained from fish WKM or snail hemolymph and in a single cell suspension, were plated in a 96-well plate at a concentration of 4 × 105 cells/well in 200 µL of medium and incubated with 2 × 107 CTV-coupled S. aureus/well for 3 h at room temperature. As control, the phagocytosis was inhibited incubating the cells + CTV-S. aureus mix either on ice (for both species) or with 0.08 mg/mL CCB for zebrafish cells or with 30 mM EDTA and 10 mM HEPES for apple snail cells  at a speed of 1,000 images/s.il cells . After 2X Mark II at 60× magnification, slow/sensitive flow speed , using 633, 488, and 405 nm laser excitation. BF was acquired on channels 1 and 9, DHR (488 nm excitation) on channel 2, CTV-S. aureus (405 nm excitation) on channel 7, Draq5 (633 nm excitation) on channel 11, and SSC was acquired on channel 6. Single-color controls were also acquired for each fluorescent channel to allow for fluorescence spillover correction.Following cell preparation, data were acquired from each sample on the ImageStreamX Mark II system  were compensated (spillover and other corrections applied), background was subtracted, and features were calculated using IDEAS 6.2 software . The resulting compensated image files (CIF files) were used to quantify features for all cells and samples. An interactive map representing the pipeline, the software used, the format of the exported files, and an approximation of time required for running the individual steps is provided in i.e., F1 scores), not requiring for a priori knowledge of the number of clusters (populations) and having reasonable runtimes (due to hardware CPU requirements). We also did an early comparison of X-shift with K-means (data not shown) and determined that K-means was insufficient for our purposes as known cell populations were not well represented by clusters, and we did not want to specify the number of expected clusters into the method’s input parameters since this would not be known in experimental use. During the import into VorteX, all features were scaled to 1 SD to equalize the contribution of features towards clustering. Clustering was performed in VorteX testing a range of k values , choosing a final k value using the ‘find elbow point for cluster number’ function in VorteX and confirming visually that over- or underclustering did not occur. FDL graphs of a subset of cells obtained from each set of samples were also generated in VorteX, and cell coordinates in the resultant 2D space were exported along with graphML representation of the FDL graph. Finally, tabular data (CSV files) was exported from VorteX including a master table of every cell event with its cluster assignment and original sample ID, as well as a table of the average feature values for each cluster and counts of cells per cluster and per sample.These processed data files were then imported into the VorteX clustering environment for X-Shift k-nearest-neighbor clustering  . X-ShiftClustering results were further analyzed and plotted in R  by mergiDAF files were opened in FCS Express Plus software, and the ‘R add parameters’ transformation feature with a custom script was used to merge the clustering data saved in the CSV files generated above with both DAF and CIF files . FCS Express Plus was utilized at this stage of work because it is the only platform currently available that works with Amnis DAF and CIF files while also running transformation processes driven by R scripting. ImageJ Bio-Formats allows reading images from DAF and CIF files, but we got pixels with a value much higher than expected, probably due to a bug that has not been fixed yet. This allowed to visualize image galleries of cells within each cluster and gate by features of interest on 2D plots  for exploring the clustering results and identifying clusters and populations of interest. FCS Express Plus is a proprietary software, but similar results can be obtained with IDEAS software where a text file with cluster IDs for each image event can be imported and the cluster information can be matched to the event images.https://github.com/stowersinstitute/LIBPB-1390-Image3C reports a detailed description of all these processing steps and includes sample scripts, workflow files, and example datasets and tutorials.The full complement of R packages used includes flowCore , flowStade novo clustering analysis, we used a CNN  using IDEAS 6.2 software.Once the clusters were defined with the previously described ed a CNN  based oned a CNN  for imagX Mark II system have nonuniform sizes, each image was cropped or padded to 32 × 32 pixels using NumPy indexing  was used.Negative binomial regression was performed on tables of cell counts per cluster and per sample and plots were generated using R  with the public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) eLife…. Your article has been reviewed by 2 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Didier Stainier as the Senior Editor.Thank you for submitting your article "Image3C: a multimodal image-based and label independent integrative method for single-cell analysis" for consideration by Both reviewers have agreed that your submitted manuscript is a valuable contribution to the field of label-free assessment of complex cellular mixtures and should be published given some crucial revisions.The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) Compare and contrast Image3C with other label-free phenotyping approaches, an issue raised by all reviewers.2) The software should be made more accessible for the general user.3) Please address the concerns regarding the assignment post CNN being "unsupervised", which was raised by both reviewers.4) Address other options for Image3C workflow tasks . – At a minimum, explain the choices more criticallyAlso, please address further comments below and correct/clarify figures as appropriate (see comments)Reviewer #1 (Recommendations for the authors):– Given this is a tool and resource paper, I think more attention should be placed in making the software accessible to the general users. In the github page, there are multiple mentions of 'If on site at SIMR' , which did not inspire confidence that this tool is designed for use by a wide audience.– Can the authors compare and clarify the contribution of Image3C against other label-free single cell image-based phenotyping systems?– Since there are many choice of algorithms in each step of the pipeline, such as feature extraction, image processing, clustering, etc. Have the authors performed a comparison of several sensible methods to show that their choice is the most suitable? At the minimum, the authors should explain their choice more critically.Reviewer #2 (Recommendations for the authors):D. rerio WKM) and a non-model system (Hemolymph from the apple snail): Not only do they present consistent clustering, but also the emergence of phagocytes upon infection. I can appreciate that Image3C is not only applicable without a host of established reagents, but that it might add an additional layer based on cell morphology, rather than pure transcriptomics (or genome accessibility) – these layers of information may be complimentary, which I suggest the authors make a point of.The authors present a convincing argument for the use of imaging flow cytometer data in profiling and comparing complex cell mixtures, such as dissociated tissues. I especially appreciated the application of Image3C to separate datasets from fish (The code is largely available (except the steps using proprietary software), which is good. However, I believe the code is currently too disjointed to be useful to a non-expert. The workflow currently involves steps using the Amnis IDEAS package, followed by custom scripts in R, followed by VorteX (I believe in Java) followed by more scripts in R, followed by optional classification using CNNs in python, with a side branch of operations using commercial flow cytometry software (FCSexpress). I think it would be advisable to somehow package this assemblage of scripts into a more accessible and user-friendly package. For example, open source packages like FlowCore might be integratable.Along the same lines, are there any alternatives for depending on the specific instrument (Amnis ImageStream Mark2) and the proprietary IDEAS software?As is, I was unable to test the packages for lack of instrument-specific data, software.Sample data should be provided together with a testable, streamlined software package as well.The authors have not sufficiently contextualized published work on label-free extraction of informative image features. Nor have they compared Image3C performance !The authors should highlight and illustrate the reproducibility of the data across independent data sets (assuming this is true) – After going through the Materials and methods, I realized that several replicates were generated for each data set. Supplemental figures attest to that.I am still not clear on how exactly cell type identities were assigned to the FDL clusters. It is my understanding that a lot of prior knowledge (and corresponding studies) was (were) necessary for assignment. As such, analysis using Image3C may be less useful for assessing cell type complexity, but rather for changes therein. The claim of "cell types are identified by de novo clustering" should then be toned down.Furthermore, the statement that"…this produces a CNN-based cell classifier 'machine' used to quantify subsequently acquired image-based flow cytometry data and to compare cellular composition of samples across multiple experiments, in a high-throughput and unsupervised manner…„is utterly bewildering to me. The CNN utilized cluster features as assignment guides. I believe that this is supervised by definition."I was unable to sufficiently evaluate some of the figures, in particular the imaged cell arrays in various channels. In many cases the levels are such that signal I have to assume to be there is not visible. This is particularly true for the Phagocytosis assay, where DHR and CTV signal should be visible. Also, in these cases a direct comparison with sister cluster cells should be shown.Check labels  for clarity (e.g. "Single and Nuc" in S3?) Essential revisions:1) Compare and contrast Image3C with other label-free phenotyping approaches, an issue raised by all reviewers.We included in the manuscript a new table (Table 1) where we compare label-free phenotyping and cell clustering approaches. We took into consideration on which samples the tool was tested, the need of prior knowledge of the sample and/or species-specific reagents at any point of the process, and the hardware and software required. We also further discussed these aspects in the main text.2) The software should be made more accessible for the general user.To make our tool more accessible, we have: (1) thoroughly revised the readme document in the GitHub page making it clearer and more detailed; (2) added tutorial videos to the GitHub page to show how to use some of the mentioned software; (3) saved the R code as a markdown page to make it more user friendly; (4) renamed the example files that can be used to test our pipeline to make them more self-explanatory; (5) saved the Python code in the more user friendly Jupyter Notebooks; and (6) transformed Figure 1—figure supplement 1 in an interactive map where it is possible to click on the different steps of the Image3C pipeline and be automatically directed to the corresponding section of our GitHub repository page.3) Please address the concerns regarding the assignment post CNN being "unsupervised", which was raised by both reviewers.We thank both reviewers for pointing this out and we apologize for the mistake. In the text, we rephrased all the sentences in which the CNN was mistakenly defined as “unsupervised”.4) Address other options for Image3C workflow tasks . – At a minimum, explain the choices more critically.We thank the reviewers for this comment. We did not perform a side-by-side comparison between several methods for each step of the pipeline, but we chose the algorithms and software based on published literature, pilots we performed, and availability of options based on the task. We now include in the Material and Methods a more detailed explanation the criteria used for our choices and we now mention an alternative to FCS Express that is the only software used that is not free or open source.Also, please address further comments below and correct/clarify figures as appropriate (see comments).Reviewer #1 (Recommendations for the authors):– Given this is a tool and resource paper, I think more attention should be placed in making the software accessible to the general users. In the github page, there are multiple mentions of 'If on site at SIMR' , which did not inspire confidence that this tool is designed for use by a wide audience.We completely agree with this comment and we apologize for the oversights. We removed all the references to SIMR and we included a number of changes aimed at making the pipeline more accessible. We have: (1) thoroughly revised the readme document in the GitHub page making it clearer and more detailed; (2) added tutorial videos to the GitHub page to show how to use some of the key software mentioned in the manuscript; (3) saved the R code as a markdown page to make it more user friendly; (4) renamed the example files that can be used to test our pipeline to make them more self-explanatory; (5) saved the Python code in the more user friendly Jupyter Notebooks; (6) transformed Figure 1-supplement figure 1 in an interactive map where it is possible to click on the different steps of the Image3C pipeline and be automatically directed to the corresponding section of our GitHub repository page.– Can the authors compare and clarify the contribution of Image3C against other label-free single cell image-based phenotyping systems?We included in the manuscript a new table (Table 1) where we compare label-free phenotyping and cell-clustering approaches. We took into consideration on which samples the tool was tested, the need for prior knowledge of the sample and/or species-specific reagents at any point of the process, and the hardware and software required. We also further discuss these aspects in the main text.– Since there are many choice of algorithms in each step of the pipeline, such as feature extraction, image processing, clustering, etc. Have the authors performed a comparison of several sensible methods to show that their choice is the most suitable? At the minimum, the authors should explain their choice more critically.We did not perform a side-by-side comparison between several methods for each step of the pipeline, but we chose the algorithms and software based on published literature, pilots we performed and availability of options based on the task. We now include in the Material and Methods a more detailed explanation for our choices and we mention an alternative to FCS Express which is the only software used that is not free or open source.Reviewer #2 (Recommendations for the authors):D. rerio WKM) and a non-model system (Hemolymph from the apple snail): Not only do they present consistent clustering, but also the emergence of phagocytes upon infection. I can appreciate that Image3C is not only applicable without a host of established reagents, but that it might add an additional layer based on cell morphology, rather than pure transcriptomics (or genome accessibility) – these layers of information may be complimentary, which I suggest the authors make a point of.The authors present a convincing argument for the use of imaging flow cytometer data in profiling and comparing complex cell mixtures, such as dissociated tissues. I especially appreciated the application of Image3C to separate datasets from fish , which is good. However, I believe the code is currently too disjointed to be useful to a non-expert. The workflow currently involves steps using the Amnis IDEAS package, followed by custom scripts in R, followed by VorteX (I believe in Java) followed by more scripts in R, followed by optional classification using CNNs in python, with a side branch of operations using commercial flow cytometry software (FCSexpress). I think it would be advisable to somehow package this assemblage of scripts into a more accessible and user-friendly package. For example, open source packages like FlowCore might be integratable.To make our tool more accessible, we have: (1) thoroughly revised the readme document in the GitHub page making it clearer and more detailed; (2) added tutorial videos to the GitHub page to show how to use some of the mentioned software; (3) saved the R code as a markdown page to make it more user friendly; (4) renamed the example files that can be used to test our pipeline to make them more self-explanatory; (5) saved the Python code in the more user friendly Jupyter Notebooks; and (6) transformed Figure 1-supplement figure 1 in an interactive map where it is possible to click on the different steps of the Image3C pipeline and be automatically directed to the corresponding section of our GitHub repository page. We now include in the Material and Methods an explanation for our choices of software/algorithms and we mention an alternative to FCS Express that is the only software used that is not free or open source.Along the same lines, are there any alternatives for depending on the specific instrument (Amnis ImageStream Mark2) and the proprietary IDEAS software? As is, I was unable to test the packages for lack of instrument-specific data, software. Sample data should be provided together with a testable, streamlined software package as well.We thank the reviewer for this comment. We uploaded in the GitHub an example dataset for users to test the pipeline without having to collect data themselves. We also uploaded files saved at intermediate steps of analysis so the users can test the steps following the IDEAS software. Once the users are ready to perform the experiments and they have an Imagestream available, then the IDEAS software can be freely downloaded.The authors have not sufficiently contextualized published work on label-free extraction of informative image features. Nor have they compared Image3C performance!We have now included in the manuscript a new table (Table 1) where we compare label-free phenotyping and cell clustering approaches. We took into consideration on which samples the tool was tested, the need for prior knowledge of the sample and/or species-specific reagents at any point of the process, and the hardware and software required. We also further discuss these aspects in the main text.Concerning the comparison of performance, Image3C was developed because of the lack of published tools able to perform de novo clustering without the use of specific cell-type markers. We also included, in the Material and Methods, the rationale for selecting these specific algorithms as part of the pipeline and, in the Discussion, we now mention that the true positive rate we obtain after CNN training is comparable to the rate reported in other trained neural networks.The authors should highlight and illustrate the reproducibility of the data across independent data sets (assuming this is true) – After going through the Materials and methods, I realized that several replicates were generated for each data set. Supplemental figures attest to that.We thank the reviewer for mentioning the replicates aspects. The number of replicates per datasets are reported in the figure legends and in the Material and Methods. We now include comments about the reproducibility between replicates in the Results and we include 3 new supplementary figures, that, together with Figure 2—figure supplement 2, illustrate the correlation between samples and potential outliers .I am still not clear on how exactly cell type identities were assigned to the FDL clusters. It is my understanding that a lot of prior knowledge (and corresponding studies) was (were) necessary for assignment. As such, analysis using Image3C may be less useful for assessing cell type complexity, but rather for changes therein. The claim of "cell types are identified by de novo clustering" should then be toned down.We thank the reviewer for pointing this out. We completely agree that assigning cell types is a long-term goal and that additional experiments  and examination of published literature will be necessary for achieving that. In the text, we toned down our statements, highlighting the possibility of comparing cluster abundance while making it clear that only a combination of cell morphologies, cell functions and comparison with data published in closely related organisms will allow for definitive cell type identification.Furthermore, the statement that"…this produces a CNN-based cell classifier 'machine' used to quantify subsequently acquired image-based flow cytometry data and to compare cellular composition of samples across multiple experiments, in a high-throughput and unsupervised manner…„is utterly bewildering to me. The CNN utilized cluster features as assignment guides. I believe that this is supervised by definition."We thank the reviewer for pointing this out. We completely agree and have removed the word “unsupervised” from the mentioned sentence.I was unable to sufficiently evaluate some of the figures, in particular the imaged cell arrays in various channels. In many cases the levels are such that signal I have to assume to be there is not visible. This is particularly true for the Phagocytosis assay, where DHR and CTV signal should be visible. Also, in these cases a direct comparison with sister cluster cells should be shown.We thank the reviewer and we apologize for the difficulties encountered in evaluating the signal in the cell images. Unfortunately, FCS Express, the software that we used to combine cell images and their cluster IDs and produce the cell galleries  uses the information obtained from IDEAS and does not allow display adjustments (intensity/brightness/color) of the images. We agree that the color used for the bacterial signal is not giving the best contrast against the background and we apologize for the limited possibilities in adjusting the image features using FCS Express. As suggested from the reviewer, we included in the manuscript a new Supplementary Figure  showing in yellow the signal of the bacteria labelled with cell-trace-violett and comparing side-by-side professional phagocytes and non-phagocytic hemocytes from the apple snail. These images were obtained using the Gallery function of the Amnis IDEAS software and the value for the signal intensity is reported on top of each individual image. In addition, we now highlight in the legends of Figures 2, 3, 4 and 5 that a representation of cells belonging to each cluster has been shown in the Supplementary Files, where there is no space limitation.Check labels  for clarity (e.g. "Single and Nuc" in S3 ?)We checked all the labels in the Figures, Supplementary Files and we modified those that could be unclear to readers and users, such as labels in Figure 2—figure supplement 2  and Supplementary File 3.


Raphanus sativus, Brassicaceae) plants to explore whether flower color is influenced by herbivory, and to determine whether flower color is associated with transgenerational chromatin modifications. We found transgenerational effects of herbivory on flower color, partly related to chromatin modifications. Given the presence of herbivory in plant populations worldwide, our results are of broad significance and contribute to our understanding of flower color evolution.Variation in flower color due to transgenerational plasticity could stem directly from abiotic or biotic environmental conditions. Finding a link between biotic ecological interactions across generations and plasticity in flower color would indicate that transgenerational effects of ecological interactions, such as herbivory, might be involved in flower color evolution. We conducted controlled experiments across four generations of wild radish ( Flower color is a key trait in angiosperm evolution. It is one of the most conspicuous traits under natural selection because of its potential role in attracting pollinators, and the subsequent influence on reproductive fitness . Flower Color in flowers is mainly due to the presence of pigments . Among pMoricandia arvensis is due to within-individual plasticity produced by seasonal changes in climatic conditions  across four generations. This annual species shows a petal color polymorphism  is a naturalized, self-incompatible, herbaceous annual weed that grows along coastal sites, roadsides, and agricultural fields  feed on 1, F2, and F3) were raised. The plants used in this study are all descended from half siblings. To test our hypothesis, it was important to control for genetic variation so that any potential epigenetic or plastic mechanisms found could be interpreted and, at least partially, treated independently from genetic variation.We raised four generations of offspring from a single maternal family displaying white flowers collected from the wild in California. One maternal family (P) was raised in the greenhouse where they experienced no herbivory. After that, three generations  68 of them with mothers attacked by herbivores and 62 of them with mothers not exposed to herbivores. This design enabled us to study the effect of herbivory across generations on subsequent flower color.For all generations, we subjected plants to one of two treatments: herbivory or control (non-herbivory). We used a fully crossed, factorial experimental design, where the first factor was the exposure to herbivory in the Fad libitum. Caterpillars arrived during their second or third instar and were used in the experiments immediately upon arrival. Two Pieris rapae caterpillars were placed on the leaves of each plant in the herbivory treatment and allowed to roam around the plant freely for 2 weeks.Plants were grown in germination flats in a greenhouse at the Stanford University Greenhouse Facility. We used a soil mix of 5% fine white sand, 20% potting soil and 75% peat moss . Two weeks after germination, seedlings were transferred to 0.8-L pots. Plants were watered once a day and maintained at a 26°C/21°C, under a 12 h/12 h cycle. At the beginning of the experiment, the plants had two leaves. The caterpillars used were all second or third instar. The experiment lasted 2 weeks, until larvae started pupation. The amount of leaf tissue eaten was about 60% of the total leaf area on average but ranged from 40 to 80%. The caterpillars heavily attacked the plants and the plants quickly grew new leaf tissue in response. No plants were killed by the effects of herbivory. The caterpillars were purchased and shipped from Carolina Biological Company (CBC) and were delivered in small plastic containers with five to six larvae per container, and an agar and wheat-based food medium provided 3 generation, four groups with 24 to 44 plants per group depending on the F1 and F2 treatment combination, were used. The goal was to pollinate all flowers on each plant. Several seeds per plant were sown and when all plants had germinated seeds, one seedling from each mother was selected using seedlings with similar sizes for all plants. We did not observe any plants with bronze flowers, and yellow forms were rare; thus, we focused on the production of anthocyanins (and we did not study carotenoid plasticity). Petal color of individuals was visually recorded as purple or white .The herbivory treatment lasted for about the first quarter of the plants’ life. Plants germinated after 4 or 5 days of sowing the seeds, they flowered around 45 days later, and ended life with mature seeds at 2 months. Pollination was performed by hand with makeup brushes. In each generation, plants from the same treatment were crossed. We collected pollen with the brush from all plants per group and we pollinated all plants within the same group. For the F2 generation plants before and after treatment. Plants were harvested at approximately 1 week of age before the treatment, and again at 3 weeks of age after the treatment. Plants had two or three leaves in the first sampling and between 5 and 10 leaves in the second sampling. Only fresh leaves that had recently expanded were sampled. We cut out one 2.7 cm diameter disc with a cork borer from a leaf, and leaf material was kept in dry silica gel until DNA extraction. The epigenetic characterization of the individuals before and after the treatment was made by means of methylation sensitive amplification fragment length polymorphism. In this technique, genomic DNA is digested by methylation-sensitive enzymes providing an epigenetic fingerprint for the plants.We collected leaf material from FHpaII. HpaII cleaves CCGG sequences when cytosines are not methylated. Cleaving may be impaired when at least one cytosine is hemi-methylated and this process is inactive if one (or both) of the cytosines are fully methylated . Thus, a simplified MSAP method was performed using only primer combinations with the methylation-sensitive thylated . Thus, w2 generation using 2 primer combinations. The selected combinations X-AC/M-AC and X-AC/M-ATC were used for fragment amplification. MS-AFLP analyses were carried out by Keygene Laboratories (Netherlands).DNA was isolated using the hexadecyl-trimethyl-ammonium-bromide procedure. After that, MSAFLP fingerprinting was performed. We first performed a pilot study with 15 individuals (30 samples) and 6 primer combinations. Based on this approach, we analyzed 106 samples of 53 plants belonging to the F3 was performed to relate the anthocyanin presence (with or without anthocyanin) to the herbivory treatment in F1, F2, and F3 generations. We additionally tested this model including two- and three-way interactions among generation treatments. An alternative model was tested in which the fixed factors included the number of herbivory events across generations and the order of the herbivory treatments across generations (nested in number of herbivory events). All models were compared by means of AICc criterion and the model which presented the best fit included exclusively the herbivory treatment in each generation.A generalized linear model  for the 130 plants from FN = 53 plant individuals) by marker (N = 402) score matrix was obtained where each element denoted whether the sample involved had experienced a methylation event (score = 1) or not (score = 0) in the corresponding marker . We assessed the genome-wide methylation per individual as the percentage of methylation events occurring per individual during treatment.Only fragments >300 base pairs in size were considered to reduce the potential impact of size homoplasy . Methyla2 plants affected flower color in the F3 generation. A generalized linear model  for the response variable anthocyanin presence or absence (1/0) in the F3 generation was performed to relate this pigment presence to the global methylation of their mothers during treatments (fixed covariate included in the model). We tested alternative models in which herbivory treatments across generations were also included either independently or as the number of inductions in the ancestry line and we selected the model in which only the covariate methylation was included based on the AICc criterion.To understand the potential mechanism of transgenerational plasticity in flower color, we analyzed how methylation in FStatistical analyses were performed in RStudio for R version 4.0.2 . Models 3 plants, 94 individuals had white flowers (without anthocyanins) and 36 individuals had pink/purple flowers (with anthocyanins). The presence/absence of anthocyanins in these 130 (F3) individuals was related to the maternal herbivory treatment . Global genome-wide cytosine methylation of F2 plants was positively related to the probability anthocyanins presence in their offspring   . Additioe plants . We havee plants  that thee plants . The ecoe plants , 2020.The consequences of transgenerational plasticity through shared pathways and potential pleiotropies with traits that affect reproductive capabilities in offspring, remain understudied but potentially significant . Our worThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.MS and RD designed the study. MS and IN analyzed the data and performed the experimental work. MS and EN wrote the manuscript. All authors contributed to the revisions.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Survival of patients with metastatic melanoma varies widely. Melanoma is a highly proliferative, chemo-resistant disease. With the recent availability of immunotherapies such as checkpoint inhibitors, durable response rates have improved but are often still limited to 2–3 years. Response rates to treatment range from 30 to 45% with combination therapy however no improvement in overall survival is frequently observed. Of the available therapies, many have targeted the BRAFV600E mutation that results in abnormal MAPK pathway activation which is important for regulating cell proliferation. Immune checkpoint inhibitors such as anti-PD-1 and anti-PD-L1 offer better success but response rates are still low. Identifying biomarkers to better target those who will respond and identify the right combination of treatment is the best approach. In this study, we utilize data from the Cancer Cell Line Encyclopedia (CCLE), including 62 samples, to examine features of gene expression (19K+) and copy number (20K+) in the melanoma cell lines. We perform a clustering analysis on the feature set to assess genetically similarity among the cell lines. We then discover which specific genes and combinations thereof maximize cluster density. We design a feature selection approach for high-dimensional datasets that integrates multiple disparate machine learning techniques into one cohesive pipeline. Our approach provides a small subset of genes that can accurately distinguish between the clusters of melanoma cell lines across multiple types of classifiers. In particular, we find only the 15 highest ranked genes among the original 19 K are necessary to achieve perfect or near-perfect test split classification performance. Of these 15 genes, some are known to be linked to melanoma or other cancer progressions, while others have not previously been linked to melanoma and are of interest for further examination. Melanoma can be a devastating disease, one in which incidence is on the rise, and treatment options are limited for the most aggressive forms of the disease. Several gene mutations are widely expressed in melanoma incidences. In particular, the BRAFV600E mutation occurs in ~60% of patients. This mutation results in constitutive activation of BRAF signaling in the ERK/MAP Kinase pathway with a result of increased cell proliferation and survival . Having large datasets available can provide a powerful tool for identifying pathways and gene expressions that are critical in determining prognosis and overall sensitivity to treatments. In this study, we have selected the melanoma cell lines from the high-dimensional CCLE dataset and used various machine learning methods to identify genes of interest that distinguish these 62 cell lines into three discrete clusters. To date, the use of biomarker data from these databases has provided limited success and will require confirmation in biologic systems to determine true correlative benefits. The overall hope is that these analyses will lead to the development of molecular signatures for targeted therapies and the ability to individualize the treatments for the most durable response.One approach to address this question is to profile gene expression using CDNA microarray screening or transcriptome analyses from patient tumor libraries. These have the advantage of providing known information on tumor response to different therapies, tumor staging, prognosis, and survival data. The downside to this approach is limited access to tissues, tissue heterogeneity, and overall tissue availability -competitive with the optimal clustering obtained by augmenting k-means with an improved initialization technique. Computational experiments showed that the augmentation improves both the speed and the accuracy of k-means, often quite dramatically. Likely due to the extreme scarcity and sparseness of our data, more complex clustering techniques did not prove useful.We used gene expression and copy number features for 62 melanoma cell lines from the CCLE to systematically assess features of interest for distinguishing genomic clusters of melanoma cell lines. CCLE skin cancer data included expression for >19,000 genes, copy number for >20,000 genes and 24 drug sensitivity features. To identify the distinct clusters of the cell lines, we applied the k-means++  which reflects the sum of Euclidean distances of cluster members to cluster centers. An elbow-point with this metric suggests a natural k in the data such that the clustering explains a large percentage of variability in the data with a small k. Each data point corresponding to each candidate k reflects the results of ten randomized trials. The solution with the minimum error is selected.When plotting a goodness-of-fit metric against the number of clusters specified for the k-means++ algorithm, we can search for a specific number of clusters, k when considering the copy number variation features alone indicated by a near-straight line in the graph. Combining the copy number variations with the gene expressions, shown in As shown in Information from the American Type Culture Collection (ATCC) suggested the origin of the two cell lines exclusively clustered together in To identify the patterns of gene expressions best separating the three clusters, we applied a pipeline of various feature selection and classification methods with cluster index used as the response variable. We tailored our methods to work with our high-dimensional dataset.Feature selection methods consist of three main groups: filter, wrapper, and embedded methods. Filter methods rank each feature according to some univariate metric with the response variable. Wrapper algorithms search for the best subset of features concerning the classification performance of an underlying model. The wrapper algorithm typically treats the classification algorithm as a black box, such that any classifier is suitable for the wrapper. One can use standard optimization techniques  as the wrapper algorithm. Embedded methods search among different feature subsets, but unlike wrappers, the process is embedded into a classification model and takes place naturally as a part of learning the classifier. Decision Trees are a famous example of a classification algorithm with embedded feature selection properties. For more information on feature selection methods and their applications to genomic and proteomic data, refer to Dubitz  and the k-nearest neighbors (KNN) algorithm. Specifically, the objective function of the search is the average test set classification performance of KNN when k is varied from three to nine. We randomize the training and test splits for each trial in the last step. Each random trial of the search algorithm returns a 20 feature subset of the 500 feature pool. We record the features which occur in the results the most often. This procedure consolidates the results from three highly varied algorithms.In part because of the high dimensionality and sparseness of the CCLE melanoma data, one is often led to finding entirely different results from applying feature selection on randomized trials or between different algorithms. The results depend entirely on algorithm details and not the structures within the data itself. Also, in part because of the power of various classification algorithms: using a large enough subset of features, even if they are simply randomly selected, can yield good classification accuracy. The classifier can learn patterns in the data that are circumstantial artifacts as opposed to biologically relevant information. We propose that the most general features which distinguish the data will score well across different kinds of feature selection algorithms and will also yield high classification performances across many different types of classifiers. To reach this end, we first employ a statistical univariate technique, called the Fisher Score  on Input Data and select the top 500 gene expressions based on the SVM-RFE+CBR ranking.S2. Perform the SVM-RFE+CBR method  and (S2). After eliminating duplicates, the resulting reduced data contains 49 cell lines with 928 gene expressions.S3. Reduced Data and record the genes that occur most often in the resulting optimal 20 feature subsets.S4. Perform 500 randomized trials of SFFS wrapped around KNN on Output Data: 49 cell lines with the top 15 genes obtained from the randomized sequential trials of k-Nearest Neighbors in (S4).S5. ith feature is calculated asThe Fisher Score has been used in several studies on genetic datasets due to its computational simplicity  were sufficient for perfect or near-perfect classification performance across various classifiers. These genes are displayed in order of frequency (importance) in Of these 15 genes, some have links to melanoma prognosis. While other genes have links to other cancers, their relationship to melanoma was never previously shown. Here are brief descriptions of some of the genes which have recently been linked to various cancers and have the potential to serve as prognostic biomarkers:TBC1D16 a GTPase-activating protein for RAB family proteins is suggested to regulate EGFR in melanoma as a result of a hypomethylation event. This may confer poor survival but at the same time may increase BRAF and MEK response  which is currently the target of several large trials showing substantial benefit with anti-PD-L1 for late-stage melanoma  is encoded by PIK3CD. PIK3CD has been reported to be overexpressed in colorectal cancer (CRC) and associated with poor survival and may be a prognostic biomarker for CRC but no relation to melanoma has been observed to date  for Cluster 1. TRIM9, ARHGEF6, C16orf52, and SMTN are also lower expressing genes for Cluster 1.There are 22 cell lines that partitioned into Cluster 2. Of these, 21 are catalogued for mutations. All 21 are positive for the BRAF V600E mutation. We observe that none of these cell lines have mutations in any of the RAS genes, which stands out from the cells in Cluster 1.Examples, of preferentially essential genes in some of the cell lines in Cluster 2, as determined by CRISPR and/or RNAi screens, included BRAF, MAPK1, SOX10, MDM2, and DUSP4.Based on our analysis, TRIM9, YPEL2, ARHGEF6, SPATA13, AHR, C16orf52, and SMTN are high expressing genes present in the cell lines of Cluster 2. Of this group, RIM9, ARHGEF6, C16orf52, and SMTN are low expressing genes in cell lines of Cluster 1.depmap.org/portal). Of the six represented cells lines, five had mutations in the BRAF gene: four with the typical BRAF V600E mutation and one with BRAF I326V, which has been found in other cancers .There are seven cells lines that partitioned into Cluster 3. One is a fibroblast cell line and is not represented in the Cancer Dependency Map portal from the Broad Institute  cross-validation experiments: Randomly partition the data into k = 10 approximately equal parts. Designate one of these subsets as a test set. Build a model on the remaining k−1 = 9 subsets that form the training dataset. Then, evaluate classification performance on the test set. Finally, average performance while using each fold as the test set once. We evaluated the performance of the Decision Tree model through 10 times Analysis of the drug response data provided by the CCLE suggests that these 15 genes also play a role in predicting drug responses. The CCLE reports drug response in a subset of the cancer cell lines as ActArea—the area above the fitted dose-response curve—and includes responses across 24 different anti-cancer drugs. Of these 24 anti-cancer drugs, we focused on two MEK inhibitors. These are believed to be crucial in the treatment of melanoma. Upon analyzing the mean response of these two drugs across all three of our clusters separately, we found a decreasing response from cluster 1 to cluster 3 across both drugs as shown in Through a machine learning pipeline of clustering and feature selection, our study identifies 15 genes whose levels of expression categorize melanoma into three genomic clusters. A Decision Tree model needs only three genes—GSTO1, TBC1D16, and TRIM9—to reliably classify nearly all of the cell lines into their respective clusters. These results shed some light on the globally relevant genetic intravariability among melanoma cell lines. These results may indicate extra considerations in choosing a cancer therapy in addition to the treatment of the BRAF mutation that exists in 42/48  of the CCLE cell lines across all clusters  Research Experience for Undergraduates (REU) Grant no. 1359341. Publication of this article was funded in part by the Open Access Subvention Fund and the John H. Evans Library.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The nuclear basket (NB) scaffold, a fibrillar structure anchored to the nuclear pore complex (NPC), is regarded as constructed of polypeptides of the coiled-coil dominated protein TPR to which other proteins can bind without contributing to the NB’s structural integrity. Here we report vertebrate protein ZC3HC1 as a novel inherent constituent of the NB, common at the nuclear envelopes (NE) of proliferating and non-dividing, terminally differentiated cells of different morphogenetic origin. Formerly described as a protein of other functions, we instead present the NB component ZC3HC1 as a protein required for enabling distinct amounts of TPR to occur NB-appended, with such ZC3HC1-dependency applying to about half the total amount of TPR at the NEs of different somatic cell types. Furthermore, pointing to an NB structure more complex than previously anticipated, we discuss how ZC3HC1 and the ZC3HC1-dependent TPR polypeptides could enlarge the NB’s functional repertoire. Saccharomyces cerevisiae )th, USA; ) was useX. laevis oocytes in 5:1-H buffer (see above) at 18 °C, the preparation of the annulate lamellae (AL)-containing fraction from oocytes that were first manually defolliculated and then enucleated before the AL isolation procedure, and the isolation of cytosol from such enucleated oocytes, were in essence as described ).In addition to ZC3HC1 immunogold-labelling, antibodies against NUP153 and TPR that had turned out suitable for post-embedding iTEM were used for reference labellings again. Among these were antibodies against the NT of xlTPR and a domain segment of xlNUP153 that included part of its TPR-binding domain (TBD), known to be located at the NR e.g., ,89). Suc. Suc89])By contrast, IGPs for the NT of xlTPR were found located deeper within the nuclear interior D, with aXenopus oocyte can possess one or more additional NB-like structures (NBLS), each about 40–45 nm in length, deducible also from their initial presentation )All taken together, we see no obligatory contradiction between ZC3HC1 being a regular NB component and the instability of this relationship when exposed to certain non-physiological conditions. We actually do not regard it as unreasonable that the affinity between ZC3HC1 and the NB might even vary during naturally occurring alterations of environmental conditions. Being capable of responding to such changes, thereby rendering a usually tight interaction sufficiently flexible on demand, might allow for dynamic rearrangements that could be of physiological relevance. We also regard it as noteworthy that the biochemical stability of certain interactions between the NB and ZC3HC1 can even differ to some extent between different cell types (our unpublished data). In fact, we deem it possible that the interplay between some of the NB-resident amounts of TPR and ZC3HC1 is by default either more or less flexible in the one or other cell type, and that also signalling events can modulate these interactions.Even though the findings made in the current investigation are at variance with formerly reported perceptions regarding the function and the binding partners of ZC3HC1, we feel confident in concluding that ZC3HC1 is neither a naturally occurring component of SCF complexes nor required for the degradation of CCNB1. After having scrutinised the most conflicting issues experimentally , we compSchizosaccharomyces pombe and Saccharomyces cerevisiae, respectively , wi, wiSchizy  in r e.g., ,124). Mo. MoC. elr , which formerly have been shown to depend on TPR .In again other scenarios, one would look upon the ZC3HC1-dependent TPR polypeptides at the NB as being there in order to increase the NB’s functional repertoire in a variety of ways; for example, by enabling the NB proper to modulate its architecture and performance on demand, like by allowing the NB to more flexibly adopt different conformations, or by simply providing more TPR polypeptides as binding sites for other molecules. In this latter context, we can imagine that if an NPC-associated TPR polypeptide functions as a temporary anchor-point for a subset of genes or as a transient docking site for some but not other cargo molecules on their way to and from the NPC, the ZC3HC1-conferred stabilisation or attachment of additional TPR polypeptides would simply provide additional ones of such anchor points or docking sites close to the NPC. There they might participate in perinuclear transcription regulation, for example, of stress-responsive genes whose transcripts need to be most efficiently exported upon acute demand, or in fine-tuning distinct pathways for different types of cargos that have to transit the NB.Within these scenarios, we are also considering the ideas that such ZC3HC1-dependent additional pools of TPR polypeptides can form the kinds of additional NB-like structures that can be seen appended to the TR of the NPC-attached NB in oocytes see  and thatMoreover, we also do not consider it unrealistic that the second pool of TPR polypeptides could contribute to the formation of a meshwork, composed of TPR’s largely unstructured carboxy-terminal “tail” domain ,128 (FigFurther work will now need to address and scrutinise these different scenarios in all detail. While we have characterised ZC3HC1 in the current study as a novel genuine component of the NB that engages in an interdependent relationship with the NB’s scaffold protein TPR, coming studies will need to unveil how close and complex this interaction might be.