CellProfiler: Novel Automated Image Segmentation Procedure for Super-Resolution Microscopy

Assessment of AURKA expression and prognosis prediction in lung adenocarcinoma using machine learning-based pathomics signature

Objective

This study aimed to develop quantitative feature-based models from histopathological images to assess aurora kinase A (AURKA) expression and predict the prognosis of patients with lung adenocarcinoma (LUAD).

Methods

A dataset of patients with LUAD was derived from the cancer genome atlas (TCGA) with information on clinical characteristics, RNA sequencing and histopathological images. The TCGA-LUAD cohort was randomly divided into training (n = 229) and testing (n = 98) sets. We extracted quantitative image features from histopathological slides of patients with LUAD using computational approaches, constructed a predictive model for AURKA expression in the training set, and estimated their predictive performance in the test set. A Cox proportional hazards model was used to assess whether the pathomic scores (PS) generated by the model independently predicted LUAD survival.

Results

High AURKA expression was an independent risk factor for overall survival (OS) in patients with LUAD (hazard ratio = 1.816, 95 % confidence intervals = 1.257-2.623, P = 0.001). The model based on histopathological image features had significant predictive value for AURKA expression: the area under the curve of the receiver operating characteristic curve in the training set and validation set was 0.809 and 0.739, respectively. Decision curve analysis showed that the model had clinical utility. Patients with high PS and low PS had different survival rates (P = 0.019). Multivariate analysis suggested that PS was an independent prognostic factor for LUAD (hazard ratio = 1.615, 95 % confidence intervals = 1.071-2.438, P = 0.022).

Conclusion

Pathomics models based on machine learning can accurately predict AURKA expression and the PS generated by the model can predict LUAD prognosis.

Pathological imaging-assisted cancer gene-environment interaction analysis

Gene-environment (G-E) interactions have important implications for cancer outcomes and phenotypes beyond the main G and E effects. Compared to main-effect-only analysis, G-E interaction analysis more seriously suffers from a lack of information caused by higher dimensionality, weaker signals, and other factors. It is also uniquely challenged by the "main effects, interactions" variable selection hierarchy. Effort has been made to bring in additional information to assist cancer G-E interaction analysis. In this study, we take a strategy different from the existing literature and borrow information from pathological imaging data. Such data are a "byproduct" of biopsy, enjoys broad availability and low cost, and has been shown as informative for modeling prognosis and other cancer outcomes/phenotypes in recent studies. Building on penalization, we develop an assisted estimation and variable selection approach for G-E interaction analysis. The approach is intuitive, can be effectively realized, and has competitive performance in simulation. We further analyze The Cancer Genome Atlas (TCGA) data on lung adenocarcinoma (LUAD). The outcome of interest is overall survival, and for G variables, we analyze gene expressions. Assisted by pathological imaging data, our G-E interaction analysis leads to different findings with competitive prediction performance and stability.

Exploring and validating the prognostic value of pathomics signatures and genomics in patients with cutaneous melanoma based on bioinformatics and deep learning

BACKGROUND

Cutaneous melanoma (CM) is the most common malignant tumor of the skin. Our study aimed to investigate the prognostic value of pathomics signatures for CM by combining pathomics and genomics.

PURPOSE

The purpose of this study was to explore the potential application value of pathomics signatures.

METHODS

Pathology full scans, clinical information, and genomics data for CM patients were downloaded from The Cancer Genome Atlas (TCGA) database. Exploratory data analysis (EDA) was used to visualize patient characteristics. Genes related to a poorer prognosis were screened through differential analysis. Survival analysis was performed to assess the prognostic value of gene and pathomics signatures. Artificial neural network (ANN) models predicted prognosis using signatures and genes. Correlation analysis was used to explore signature-gene links.

RESULTS

The clinical traits for 468 CM samples and the genomic data and pathology images for 471 CM samples were obtained from the TCGA database. The EDA results combined with multiple machine learning (ML) models suggested that the top 5 clinical traits in terms of importance were age, biopsy site, T stage, N stage and overall disease stage, and the eight ML models had a precision lower than 0.56. A total of 60 differentially expressed genes were obtained by comparing sequencing data. A total of 413 available quantitative signatures of each pathomics image were obtained with CellProfile software. The precision of the binary classification model based on pathomics signatures was 0.99, with a loss value of 1.7119e-04. The precision of the binary classification model based on differentially expressed genes was 0.98, with a loss value of 0.1101. The precision of the binary classification model based on pathomics signatures and differentially expressed genes was 0.97, with a loss value of 0.2088. The survival analyses showed that the survival rate of the high-risk group based on gene expression and pathomics signatures was significantly lower than that of the low-risk group. A total of 222 pathomics signatures and 51 differentially expressed genes were analyzed for survival with p-values of less than 0.05. There was a certain correlation between some pathomics signatures and differential gene expression involving ANO2, LINC00158, NDNF, ADAMTS15, and ADGRB3, etc. CONCLUSION: This study evaluated the prognostic significance of pathomics signatures and differentially expressed genes in CM patients. Three ANN models were developed, and all achieved accuracy rates higher than 97%. Specifically, the pathomics signature-based ANN model maintained a remarkable accuracy of 99%. These findings highlight the CellProfile + ANN model as an excellent choice for prognostic prediction in CM patients. Furthermore, our correlation analysis experimentally demonstrated a preliminary link between disease quantification and qualitative changes. Among various features, including M stage and treatments received, special attention should be given to age, biopsy site, T stage, N stage, and overall disease stage in CM patients.

Survival prediction of stomach cancer using expression data and deep learning models with histopathological images

Accurately predicting patient survival is essential for cancer treatment decision. However, the prognostic prediction model based on histopathological images of stomach cancer patients is still yet to be developed. We propose a deep learning-based model (MultiDeepCox-SC) that predicts overall survival in patients with stomach cancer by integrating histopathological images, clinical data, and gene expression data. The MultiDeepCox-SC not only automatedly selects patches with more information for survival prediction, without manual labeling for histopathological images, but also identifies genetic and clinical risk factors associated with survival in stomach cancer. The prognostic accuracy of the MultiDeepCox-SC (C-index = 0.744) surpasses the result only based on histopathological image (C-index = 0.660). The risk score of our model was still an independent predictor of survival outcome after adjustment for potential confounders, including pathologic stage, grade, age, race, and gender on The Cancer Genome Atlas dataset (hazard ratio 1.555, p = 3.53e-08) and the external test set (hazard ratio 2.912, p = 9.42e-4). Our fully automated online prognostic tool based on histopathological images, clinical data, and gene expression data could be utilized to improve pathologists' efficiency and accuracy (https://yu.life.sjtu.edu.cn/DeepCoxSC).

Analyzing cell-type-specific dynamics of metabolism in kidney repair

A common drawback of metabolic analyses of complex biological samples is the inability to consider cell-to-cell heterogeneity in the context of an organ or tissue. To overcome this limitation, we present an advanced high-spatial-resolution metabolomics approach using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) combined with isotope tracing. This method allows mapping of cell-type-specific dynamic changes in central carbon metabolism in the context of a complex heterogeneous tissue architecture, such as the kidney. Combined with multiplexed immunofluorescence staining, this method can detect metabolic changes and nutrient partitioning in targeted cell types, as demonstrated in a bilateral renal ischemia-reperfusion injury (bIRI) experimental model. Our approach enables us to identify region-specific metabolic perturbations associated with the lesion and throughout recovery, including unexpected metabolic anomalies in cells with an apparently normal phenotype in the recovery phase. These findings may be relevant to an understanding of the homeostatic capacity of the kidney microenvironment. In sum, this method allows us to achieve resolution at the single-cell level in situ and hence to interpret cell-type-specific metabolic dynamics in the context of structure and metabolism of neighboring cells.

Recent Microscopy Advances and the Applications to Huntington’s Disease Research

 Huntingtin is a 3144 amino acid protein defined as a scaffold protein with many intracellular locations that suggest functions in these compartments. Expansion of the CAG DNA tract in the huntingtin first exon is the cause of Huntington’s disease. An important tool in understanding the biological functions of huntingtin is molecular imaging at the single-cell level by microscopy and nanoscopy. The evolution of these technologies has accelerated since the Nobel Prize in Chemistry was awarded in 2014 for super-resolution nanoscopy. We are in a new era of light imaging at the single-cell level, not just for protein location, but also for protein conformation and biochemical function. Large-scale microscopy-based screening is also being accelerated by a coincident development of machine-based learning that offers a framework for truly unbiased data acquisition and analysis at very large scales. This review will summarize the newest technologies in light, electron, and atomic force microscopy in the context of unique challenges with huntingtin cell biology and biochemistry.

Physiologic Targets and Modes of Action for CBL0137, a Lead for Human African Trypanosomiasis Drug Development

CBL0137 is a lead drug for human African trypanosomiasis, caused by Trypanosoma brucei Herein, we use a four-step strategy to 1) identify physiologic targets and 2) determine modes of molecular action of CBL0137 in the trypanosome. First, we identified fourteen CBL0137-binding proteins using affinity chromatography. Second, we developed hypotheses of molecular modes of action, using predicted functions of CBL0137-binding proteins as guides. Third, we documented effects of CBL0137 on molecular pathways in the trypanosome. Fourth, we identified physiologic targets of the drug by knocking down genes encoding CBL0137-binding proteins and comparing their molecular effects to those obtained when trypanosomes were treated with CBL0137. CBL0137-binding proteins included glycolysis enzymes (aldolase, glyceraldehyde-3-phosphate dehydrogenase, phosphofructokinase, phosphoglycerate kinase) and DNA-binding proteins [universal minicircle sequence binding protein 2, replication protein A1 (RPA1), replication protein A2 (RPA2)]. In chemical biology studies, CBL0137 did not reduce ATP level in the trypanosome, ruling out glycolysis enzymes as crucial targets for the drug. Thus, many CBL0137-binding proteins are not physiologic targets of the drug. CBL0137 inhibited 1) nucleus mitosis, 2) nuclear DNA replication, and 3) polypeptide synthesis as the first carbazole inhibitor of eukaryote translation. RNA interference (RNAi) against RPA1 inhibited both DNA synthesis and mitosis, whereas RPA2 knockdown inhibited mitosis, consistent with both proteins being physiologic targets of CBL0137. Principles used here to distinguish drug-binding proteins from physiologic targets of CBL0137 can be deployed with different drugs in other biologic systems. SIGNIFICANCE STATEMENT: To distinguish drug-binding proteins from physiologic targets in the African trypanosome, we devised and executed a multidisciplinary approach involving biochemical, genetic, cell, and chemical biology experiments. The strategy we employed can be used for drugs in other biological systems.

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Background

Colon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.

Methods

We downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF).

Results

There were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group.

Conclusions

These results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.

Programmed necroptosis is upregulated in low-grade myelodysplastic syndromes and may play a role in the pathogenesis

Myelodysplastic syndrome (MDS) is characterized by persistent cytopenias and evidence of morphologic dysplasia in the bone marrow (BM). Excessive hematopoietic programmed cell death (PCD) and inflammation have been observed in the bone marrow of patients with MDS, and are thought to play a significant role in the pathogenesis of the disease. Necroptosis is a major pathway of PCD that incites inflammation; however, the role of necroptosis in human MDS has not been extensively investigated. To assess PCD status in newly diagnosed MDS, we performed immunofluorescence staining with computational image analysis of formalin-fixed, paraffin-embedded BM core biopsies using cleaved caspase-3 (apoptosis marker) and necroptosis markers (receptor-interacting serine/threonine-protein kinase 1 [RIPK1], phospho-mixed lineage kinase domain-like protein [pMLKL]). Patients with MDS, but not controls without MDS or patients with de novo acute myeloid leukemia, had significantly increased expression of RIPK1 and pMLKL but not cleaved caspase-3, which was most evident in morphologically low-grade MDS (<5% BM blasts) and in MDS with low International Prognostic Scoring System risk score. RIPK1 expression highly correlated with the distribution of CD71⁺ erythroid precursors but not with CD34⁺ blast cells. We found that necroptosis is upregulated in early/low-grade MDS relative to control participants, warranting further study to define the role of necroptosis in the pathogenesis of MDS and as a potential biomarker for the diagnosis of low-grade MDS.

Enhanced method to select human oogonial stem cells for fertility research

Alternative methods to obtain mature oocytes are still needed for women with premature ovarian failure (POF). Oogonial stem cells (OSCs), found in adult ovaries, have provided insight into potential paths to treating infertility. Previously, the DDX4 antibody marker alone was utilized to isolate OSCs; however, extensive debate over its location in OSCs versus resulting oocytes (transmembrane or intracytoplasmic) has raised doubt about the identity of these cells. Separate groups, however, have efficiently isolated OSCs using another antibody marker Ifitm3 which is consistently recognized to be transmembrane in location. We hypothesized that by using anti-DDX4 and anti-IFITM3 antibodies, in combination, with MACS, we would improve the yield of isolated OSCs versus using anti-DDX4 antibodies alone. Our study supports earlier findings of OSCs in ovaries during the entire female lifespan: from reproductive age through post-menopausal age. MACS sorting ovarian cells using a the two-marker combination yielded a ~ twofold higher percentage of OSCs from a given mass of ovarian tissue compared to existing single marker methods while minimizing the debate surrounding germline marker selection. During in vitro culture, isolated cells retained the germline phenotype expression of DDX4 and IFITM3 as confirmed by gene expression analysis, demonstrated characteristic germline stem cell self-assembly into embryoid bodies, and formed > 40 µm "oocyte-like" structures that expressed the early oocyte markers GDF9, DAZL, and ZP1. This enhanced and novel method is clinically significant as it could be utilized in the future to more efficiently produce mature, secondary oocytes, for use with IVF/ICSI to treat POF patients.

Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer

OBJECTIVE

This study used histopathological image features to predict molecular features, and combined with multi-dimensional omics data to predict overall survival (OS) in high-grade serous ovarian cancer (HGSOC).

METHODS

Patients from The Cancer Genome Atlas (TCGA) were distributed into training set (n = 115) and test set (n = 114). In addition, we collected tissue microarrays of 92 patients as an external validation set. Quantitative features were extracted from histopathological images using CellProfiler, and utilized to establish prediction models by machine learning methods in training set. The prediction performance was assessed in test set and validation set.

RESULTS

The prediction models were able to identify BRCA1 mutation (AUC = 0.952), BRCA2 mutation (AUC = 0.912), microsatellite instability-high (AUC = 0.919), microsatellite stable (AUC = 0.924), and molecular subtypes: proliferative (AUC = 0.961), differentiated (AUC = 0.952), immunoreactive (AUC = 0.941), mesenchymal (AUC = 0.918) in test set. The prognostic model based on histopathological image features could predict OS in test set (5-year AUC = 0.825) and validation set (5-year AUC = 0.703). We next explored the integrative prognostic models of image features, genomics, transcriptomics and proteomics. In test set, the models combining two omics had higher prediction accuracy, such as image features and genomics (5-year AUC = 0.834). The multi-omics model including all features showed the best prediction performance (5-year AUC = 0.911). According to risk score of multi-omics model, the high-risk and low-risk groups had significant survival differences (HR = 18.23, p < 0.001).

CONCLUSIONS

These results indicated the potential ability of histopathological image features to predict above molecular features and survival risk of HGSOC patients. The integration of image features and multi-omics data may improve prognosis prediction in HGSOC patients.

Challenges facing quantitative large-scale optical super-resolution, and some simple solutions

Optical super-resolution microscopy (SRM) has enabled biologists to visualize cellular structures with near-molecular resolution, giving unprecedented access to details about the amounts, sizes, and spatial distributions of macromolecules in the cell. Precisely quantifying these molecular details requires large datasets of high-quality, reproducible SRM images. In this review, we discuss the unique set of challenges facing quantitative SRM, giving particular attention to the shortcomings of conventional specimen preparation techniques and the necessity for optimal labeling of molecular targets. We further discuss the obstacles to scaling SRM methods, such as lengthy image acquisition and complex SRM data analysis. For each of these challenges, we review the recent advances in the field that circumvent these pitfalls and provide practical advice to biologists for optimizing SRM experiments.

Integrative Models of Histopathological Image Features and Omics Data Predict Survival in Head and Neck Squamous Cell Carcinoma

Background

Both histopathological image features and genomics data were associated with survival outcome of cancer patients. However, integrating features of histopathological images, genomics and other omics for improving prognosis prediction has not been reported in head and neck squamous cell carcinoma (HNSCC).

Methods

A dataset of 216 HNSCC patients was derived from the Cancer Genome Atlas (TCGA) with information of clinical characteristics, genetic mutation, RNA sequencing, protein expression and histopathological images. Patients were randomly assigned into training (n = 108) or validation (n = 108) sets. We extracted 593 quantitative image features, and used random forest algorithm with 10-fold cross-validation to build prognostic models for overall survival (OS) in training set, then compared the area under the time-dependent receiver operating characteristic curve (AUC) in validation set.

Results

In validation set, histopathological image features had significant predictive value for OS (5-year AUC = 0.784). The histopathology + omics models showed better predictive performance than genomics, transcriptomics or proteomics alone. Moreover, the multi-omics model incorporating image features, genomics, transcriptomics and proteomics reached the maximal 1-, 3-, and 5-year AUC of 0.871, 0.908, and 0.929, with most significant survival difference (HR = 10.66, 95%CI: 5.06–26.8, p < 0.001). Decision curve analysis also revealed a better net benefit of multi-omics model.

Conclusion

The histopathological images could provide complementary features to improve prognostic performance for HNSCC patients. The integrative model of histopathological image features and omics data might serve as an effective tool for survival prediction and risk stratification in clinical practice.

Insight into Mechanobiology: How Stem Cells Feel Mechanical Forces and Orchestrate Biological Functions

The cross-talk between stem cells and their microenvironment has been shown to have a direct impact on stem cells' decisions about proliferation, growth, migration, and differentiation. It is well known that stem cells, tissues, organs, and whole organisms change their internal architecture and composition in response to external physical stimuli, thanks to cells' ability to sense mechanical signals and elicit selected biological functions. Likewise, stem cells play an active role in governing the composition and the architecture of their microenvironment. Is now being documented that, thanks to this dynamic relationship, stemness identity and stem cell functions are maintained. In this work, we review the current knowledge in mechanobiology on stem cells. We start with the description of theoretical basis of mechanobiology, continue with the effects of mechanical cues on stem cells, development, pathology, and regenerative medicine, and emphasize the contribution in the field of the development of ex-vivo mechanobiology modelling and computational tools, which allow for evaluating the role of forces on stem cell biology.

Histopathological Imaging⁻Environment Interactions in Cancer Modeling

Histopathological imaging has been routinely conducted in cancer diagnosis and recently used for modeling other cancer outcomes/phenotypes such as prognosis. Clinical/environmental factors have long been extensively used in cancer modeling. However, there is still a lack of study exploring possible interactions of histopathological imaging features and clinical/environmental risk factors in cancer modeling. In this article, we explore such a possibility and conduct both marginal and joint interaction analysis. Novel statistical methods, which are “borrowed” from gene–environment interaction analysis, are employed. Analysis of The Cancer Genome Atlas (TCGA) lung adenocarcinoma (LUAD) data is conducted. More specifically, we examine a biomarker of lung function as well as overall survival. Possible interaction effects are identified. Overall, this study can suggest an alternative way of cancer modeling that innovatively combines histopathological imaging and clinical/environmental data.

Examination of Independent Prognostic Power of Gene Expressions and Histopathological Imaging Features in Cancer

Cancer prognosis is of essential interest, and extensive research has been conducted searching for biomarkers with prognostic power. Recent studies have shown that both omics profiles and histopathological imaging features have prognostic power. There are also studies exploring integrating the two types of measurements for prognosis modeling. However, there is a lack of study rigorously examining whether omics measurements have independent prognostic power conditional on histopathological imaging features, and vice versa. In this article, we adopt a rigorous statistical testing framework and test whether an individual gene expression measurement can improve prognosis modeling conditional on high-dimensional imaging features, and a parallel analysis is conducted reversing the roles of gene expressions and imaging features. In the analysis of The Cancer Genome Atlas (TCGA) lung adenocarcinoma and liver hepatocellular carcinoma data, it is found that multiple individual genes, conditional on imaging features, can lead to significant improvement in prognosis modeling; however, individual imaging features, conditional on gene expressions, only offer limited prognostic power. Being among the first to examine the independent prognostic power, this study may assist better understanding the "connectedness" between omics profiles and histopathological imaging features and provide important insights for data integration in cancer modeling.

Super-resolution imaging reveals the sub-diffraction phenotype of Zellweger Syndrome ghosts and wild-type peroxisomes

Peroxisomes are ubiquitous cell organelles involved in many metabolic and signaling functions. Their assembly requires peroxins, encoded by PEX genes. Mutations in PEX genes are the cause of Zellweger Syndrome spectrum (ZSS), a heterogeneous group of peroxisomal biogenesis disorders (PBD). The size and morphological features of peroxisomes are below the diffraction limit of light, which makes them attractive for super-resolution imaging. We applied Stimulated Emission Depletion (STED) microscopy to study the morphology of human peroxisomes and peroxisomal protein localization in human controls and ZSS patients. We defined the peroxisome morphology in healthy skin fibroblasts and the sub-diffraction phenotype of residual peroxisomal structures (‘ghosts’) in ZSS patients that revealed a relation between mutation severity and clinical phenotype. Further, we investigated the 70 kDa peroxisomal membrane protein (PMP70) abundance in relationship to the ZSS sub-diffraction phenotype. This work improves the morphological definition of peroxisomes. It expands current knowledge about peroxisome biogenesis and ZSS pathoethiology to the sub-diffraction phenotype including key peroxins and the characteristics of ghost peroxisomes.

Super-Resolution High Content Screening and Analysis

Revealing the subcellular phenotypes at the molecular scale is critical to understand the mechanisms by which the cells function and respond to chemical treatments. Super-resolution microscopy and robust analysis tools enabled biologists to reveal and quantify phenotypes at unprecedented resolution. Developing automated imaging analysis solutions for super-resolution imaging will make high-content-screening (HCS) applicable for super-resolution microscopy, which will give access to new complex information. Here, I provide an instant automated analysis procedure for analyzing super-resolution images via CellProfiler ( www.cellprofiler.org ) platform.

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding

BACKGROUND

Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility.

NEW METHOD

Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set.

RESULTS

Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings.

COMPARISON WITH EXISTING METHODS

We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples.

CONCLUSION

The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks.

Lipid droplet subset targeting of the Drosophila protein CG2254/dmLdsdh1

Lipid droplets (LDs) are the principal organelles of lipid storage. They consist of a hydrophobic core of storage lipids, surrounded by a phospholipid monolayer with proteins attached. While some of these proteins are essential to regulate cellular and organismic lipid metabolism, key questions concerning LD protein function, such as their targeting to LDs, are still unanswered. Intriguingly, some proteins are restricted to LD subsets by an as yet unknown mechanism. This finding makes LD targeting even more complex.

Here, we characterize the Drosophila protein CG2254 which targets LD subsets in cultured cells and different larval Drosophila tissues, where the prevalence of LD subsets appears highly dynamic. We find that an amphipathic amino acid stretch mediates CG2254 LD localization. Additionally, we identified a juxtaposed sequence stretch limiting CG2254 localization to LD subsets. This sequence is sufficient to restrict a chimeric protein - consisting of the subset targeting sequence introduced to an otherwise pan LD localized protein sequence - to LD subsets. Based on its subcellular localization and annotated function, we suggest to rename CG2254 to Lipid droplet subset dehydrogenase 1 (Ldsdh1).