[Fluorescence confocal microscopy-complete digitization of pathology]. / Fluoreszenzbasierte Konfokalmikroskopie ­ vollständige Digitalisierung der Pathologie.

BACKGROUND: Fluorescence-based confocal microscopy (FCM) can be used to create virtual H&E sections in real time. So far, FCM has been used in dermato-, uro-, and gynecopathology. FCM allows the creation of a completely digitized frozen section, which could potentially replace conventional frozen sections in the future. OBJECTIVE: The aim of the current work is to implement FCM technology as a component of fully digitized processes in the pathological workflow. For this purpose, the current use of FCM in liver transplant pathology will be extended to other disciplines such as urology and otorhinolaryngology. MATERIALS AND METHODS: The FCM technique continues to be used prospectively on native tissue samples from potential donor livers. Conventional frozen sections are used comparatively to virtual FCM scans. RESULTS: The data show a nearly perfect agreement for the detection of cholangitis, fibrosis, and malignancy, and a high level of agreement for, e.g., macrovesicular steatosis, inflammation, steatohepatitis, and necrosis between virtual FCM scans and conventional routine diagnostic frozen sections. CONCLUSION: Since the availability of time- and cost-intensive frozen section diagnostics in the context of transplant pathology in continuous operation (24/7) is now only established at very few university centers in Germany due to an increasing shortage of specialists, the use of FCM could be an important building block in the current process leading towards a fully digitized pathology workflow and should thus be extended to various disciplines.

[The model transferability of AI in digital pathology : Potential and reality]. / Die Modelltransferierbarkeit von KI in der digitalen Pathologie : Potenzial und Realität.

OBJECTIVE: Artificial intelligence (AI) holds the potential to make significant advancements in pathology. However, its actual implementation and certification for practical use are currently limited, often due to challenges related to model transferability. In this context, we investigate the factors influencing transferability and present methods aimed at enhancing the utilization of AI algorithms in pathology. MATERIALS AND METHODS: Various convolutional neural networks (CNNs) and vision transformers (ViTs) were trained using datasets from two institutions, along with the publicly available TCGA-MIBC dataset. These networks conducted predictions in urothelial tissue and intrahepatic cholangiocarcinoma (iCCA). The objective was to illustrate the impact of stain normalization, the influence of various artifacts during both training and testing, as well as the effects of the NoisyEnsemble method. RESULTS: We were able to demonstrate that stain normalization of slides from different institutions has a significant positive effect on the inter-institutional transferability of CNNs and ViTs (respectively +13% and +10%). In addition, ViTs usually achieve a higher accuracy in the external test (here +1.5%). Similarly, we showcased how artifacts in test data can negatively affect CNN predictions and how incorporating these artifacts during training leads to improvements. Lastly, NoisyEnsembles of CNNs (better than ViTs) were shown to enhance transferability across different tissues and research questions (+7% Bladder, +15% iCCA). DISCUSSION: It is crucial to be aware of the transferability challenge: achieving good performance during development does not necessarily translate to good performance in real-world applications. The inclusion of existing methods to enhance transferability, such as stain normalization and NoisyEnsemble, and their ongoing refinement, is of importance.

[Challenges of automation in quantitative evaluation of liver biopsies : Automatic quantification of liver steatosis]. / Herausforderungen der Automation bei der quantitativen Auswertung von Leberbiopsien : Automatische Quantifizierung von Leberverfettung.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD), or non-alcoholic fatty liver disease (NAFLD), is a common disease that is diagnosed through manual evaluation of liver biopsies, an assessment that is subject to high interobserver variability (IBV). IBV can be reduced using automated methods. OBJECTIVES: Many existing computer-based methods do not accurately reflect what pathologists evaluate in practice. The goal is to demonstrate how these differences impact the prediction of hepatic steatosis. Additionally, IBV complicates algorithm validation. MATERIALS AND METHODS: Forty tissue sections were analyzed to detect steatosis, nuclei, and fibrosis. Data generated from automated image processing were used to predict steatosis grades. To investigate IBV, 18 liver biopsies were evaluated by multiple observers. RESULTS: Area-based approaches yielded more strongly correlated results than nucleus-based methods (⌀ Spearman rho [ρ] = 0.92 vs. 0.79). The inclusion of information regarding tissue composition reduced the average absolute error for both area- and nucleus-based predictions by 0.5% and 2.2%, respectively. Our final area-based algorithm, incorporating tissue structure information, achieved a high accuracy (80%) and strong correlation (⌀ Spearman ρ = 0.94) with manual evaluation. CONCLUSION: The automatic and deterministic evaluation of steatosis can be improved by integrating information about tissue composition and can serve to reduce the influence of IBV.

[Histomolecular classification of urothelial carcinoma of the urinary bladder : From histological phenotype to genotype and back]. / Histomolekulare Klassifikation des Urothelkarzinoms der Harnblase : Vom histologischen Phänotyp zum Genotyp und zurück.

BACKGROUND: Of all urothelial carcinomas (UCs), 25% are muscle invasive and associated with a 5-year overall survival rate of 50%. Findings regarding the molecular classification of muscle-invasive urothelial carcinomas (MIUCs) have not yet found their way into clinical practice. OBJECTIVES: Prediction of molecular consensus subtypes in MIUCs with artificial intelligence (AI) based on histologic hematoxylin-eosin (HE) sections. METHODS: Pathologic review and annotation of The Cancer Genome Atlas (TCGA) Bladder Cancer (BLCA) Cohort (N = 412) and the Dr. Senckenberg Institute of Pathology (SIP) BLCA Cohort (N = 181). An AI model for the prediction of molecular subtypes based on annotated histomorphology was trained. RESULTS: For a five-fold cross-validation with TCGA cases (N = 274), an internal TCGA test set (N = 18) and an external SIP test set (N = 27), we reached mean area under the receiver operating characteristic curve (AUROC) scores of 0.73, 0.8 and 0.75 for the classification of the used molecular subtypes "luminal", "basal/squamous" and "stroma-rich". By training on correlations to individual molecular subtypes, rather than training on one subtype assignment per case, the AI prediction of subtypes could be significantly improved. DISCUSSION: Follow-up studies with RNA extraction from various areas of AI-predicted molecular heterogeneity may improve molecular classifications and thereby AI algorithms trained on these classifications.

Multimodal analysis methods in predictive biomedicine.

For medicine to fulfill its promise of personalized treatments based on a better understanding of disease biology, computational and statistical tools must exist to analyze the increasing amount of patient data that becomes available. A particular challenge is that several types of data are being measured to cope with the complexity of the underlying systems, enhance predictive modeling and enrich molecular understanding. Here we review a number of recent approaches that specialize in the analysis of multimodal data in the context of predictive biomedicine. We focus on methods that combine different OMIC measurements with image or genome variation data. Our overview shows the diversity of methods that address analysis challenges and reveals new avenues for novel developments.

The RHOA Mutation G17V Does Not Lead to Increased Migration of Human Malignant T Cells but Is Associated with Matrix Remodelling.

Nodal T-follicular helper cell lymphoma, angioimmunoblastic-type (AITL), is characterized by constitutional symptoms, advanced-stage disease, and generalized lymphadenopathy. A genetic hallmark of this lymphoma is the frequent occurrence of the RHOA mutation G17V in neoplastic cells, which is observed in around 60% of patients. Because RHOA is involved in both T-cell receptor downstream signalling and cell migration, we hypothesized that the characteristic presentation of AITL could be the result of enhanced tumor cell migration. Therefore, this study aimed to elucidate the impact of the RHOA variant G17V on the migration of neoplastic T cells. We transfected the T-cell lymphoma cell lines HH and HuT78 to stably express the RHOA-G17V variant. RHOA-G17V-expressing T cells did not exhibit enhanced motility compared to empty-vector-transfected cells in microchannels, a 3D collagen gel, or primary human lymphatic tissue. Cells of the HH cell line expressing RHOA-G17V had an increased number of cells with cleaved collagen compared with the empty-vector-transfected cells. Therefore, we hypothesized that the early spread of AITL tumor cells may be related to remodelling of the extracellular matrix. Accordingly, we observed a significant negative correlation between the relative area of collagen in histological sections from 18 primary AITL and the allele frequency of the RHOA-G17V mutation. In conclusion, our results suggest that the characteristic presentation of AITL with early, widespread dissemination of lymphoma cells is not the result of an enhanced migration capacity due to the RHOA-G17V mutation; instead, this feature may rather be related to extracellular matrix remodelling.

CT-radiomics and clinical risk scores for response and overall survival prognostication in TACE HCC patients.

We aimed to identify hepatocellular carcinoma (HCC) patients who will respond to repetitive transarterial chemoembolization (TACE) to improve the treatment algorithm. Retrospectively, 61 patients (mean age, 65.3 years ± 10.0 [SD]; 49 men) with 94 HCC mRECIST target-lesions who had three consecutive TACE between 01/2012 and 01/2020 were included. Robust and non-redundant radiomics features were extracted from the 24 h post-embolization CT. Five different clinical TACE-scores were assessed. Seven different feature selection methods and machine learning models were used. Radiomics, clinical and combined models were built to predict response to TACE on a lesion-wise and patient-wise level as well as its impact on overall-survival prognostication. 29 target-lesions of 19 patients were evaluated in the test set. Response rates were 37.9% (11/29) on the lesion-level and 42.1% (8/19) on the patient-level. Radiomics top lesion-wise response prognostications was AUC 0.55-0.67. Clinical scores revealed top AUCs of 0.65-0.69. The best working model combined the radiomic feature LargeDependenceHighGrayLevelEmphasis and the clinical score mHAP_II_score_group with AUC = 0.70, accuracy = 0.72. We transferred this model on a patient-level to achieve AUC = 0.62, CI = 0.41-0.83. The two radiomics-clinical features revealed overall-survival prognostication of C-index = 0.67. In conclusion, a random forest model using the radiomic feature LargeDependenceHighGrayLevelEmphasis and the clinical mHAP-II-score-group seems promising for TACE response prognostication.

Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology.

BACKGROUND: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. METHODS: Retrospectively, 106 prostate tissue samples from 48 patients (mean age, [Formula: see text] years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open-source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms. RESULTS: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02-0.06 for ERG and PIN-4. CONCLUSIONS: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.

How to learn with intentional mistakes: NoisyEnsembles to overcome poor tissue quality for deep learning in computational pathology.

There is a lot of recent interest in the field of computational pathology, as many algorithms are introduced to detect, for example, cancer lesions or molecular features. However, there is a large gap between artificial intelligence (AI) technology and practice, since only a small fraction of the applications is used in routine diagnostics. The main problems are the transferability of convolutional neural network (CNN) models to data from other sources and the identification of uncertain predictions. The role of tissue quality itself is also largely unknown. Here, we demonstrated that samples of the TCGA ovarian cancer (TCGA-OV) dataset from different tissue sources have different quality characteristics and that CNN performance is linked to this property. CNNs performed best on high-quality data. Quality control tools were partially able to identify low-quality tiles, but their use did not increase the performance of the trained CNNs. Furthermore, we trained NoisyEnsembles by introducing label noise during training. These NoisyEnsembles could improve CNN performance for low-quality, unknown datasets. Moreover, the performance increases as the ensemble become more consistent, suggesting that incorrect predictions could be discarded efficiently to avoid wrong diagnostic decisions.

T-cell-derived Hodgkin lymphoma has motility characteristics intermediate between Hodgkin and anaplastic large cell lymphoma.

Classic Hodgkin lymphoma (cHL) is usually characterized by a low tumour cell content, derived from crippled germinal centre B cells. Rare cases have been described in which the tumour cells show clonal T-cell receptor rearrangements. From a clinicopathological perspective, it is unclear if these cases should be classified as cHL or anaplastic large T-cell lymphoma (ALCL). Since we recently observed differences in the motility of ALCL and cHL tumour cells, here, we aimed to obtain a better understanding of T-cell-derived cHL by investigating their global proteomic profiles and their motility. In a proteomics analysis, when only motility-associated proteins were regarded, T-cell-derived cHL cell lines showed the highest similarity to ALK- ALCL cell lines. In contrast, T-cell-derived cHL cell lines presented a very low overall motility, similar to that observed in conventional cHL. Whereas all ALCL cell lines, as well as T-cell-derived cHL, predominantly presented an amoeboid migration pattern with uropod at the rear, conventional cHL never presented with uropods. The migration of ALCL cell lines was strongly impaired upon application of different inhibitors. This effect was less pronounced in cHL cell lines and almost invisible in T-cell-derived cHL. In summary, our cell line-derived data suggest that based on proteomics and migration behaviour, T-cell-derived cHL is a neoplasm that shares features with both cHL and ALCL and is not an ALCL with low tumour cell content. Complementary clinical studies on this lymphoma are warranted.

Deep learning based on hematoxylin-eosin staining outperforms immunohistochemistry in predicting molecular subtypes of gastric adenocarcinoma.

In gastric cancer (GC), there are four molecular subclasses that indicate whether patients respond to chemotherapy or immunotherapy, according to the TCGA. In clinical practice, however, not every patient undergoes molecular testing. Many laboratories have used well-implemented in situ techniques (IHC and EBER-ISH) to determine the subclasses in their cohorts. Although multiple stains are used, we show that a staining approach is unable to correctly discriminate all subclasses. As an alternative, we trained an ensemble convolutional neuronal network using bagging that can predict the molecular subclass directly from hematoxylin-eosin histology. We also identified patients with predicted intra-tumoral heterogeneity or with features from multiple subclasses, which challenges the postulated TCGA-based decision tree for GC subtyping. In the future, deep learning may enable targeted testing for molecular subtypes and targeted therapy for a broader group of GC patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Landscape of 4D Cell Interaction in Hodgkin and Non-Hodgkin Lymphomas.

Profound knowledge exists about the clinical, morphologic, genomic, and transcriptomic characteristics of most lymphoma entities. However, information is currently lacking on the dynamic behavior of malignant lymphomas. This pilot study aimed to gain insight into the motility of malignant lymphomas and bystander cells in 20 human lymph nodes. Generally, B cells were faster under reactive conditions compared with B cells in malignant lymphomas. In contrast, PD1-positive T cells did not show systematic differences in velocity between reactive and neoplastic conditions in general. However, lymphomas could be divided into two groups: one with fast PD1-positive T cells (e.g., Hodgkin lymphoma and mantle cell lymphoma; means 8.4 and 7.8 µm/min) and another with slower PD1-positive T cells (e.g., mediastinal grey zone lymphoma; mean 3.5 µm/min). Although the number of contacts between lymphoma cells and PD1-positive T cells was similar in different lymphoma types, important differences were observed in the duration of these contacts. Among the lymphomas with fast PD1-positive T cells, contacts were particularly short in mantle cell lymphoma (mean 54 s), whereas nodular lymphocyte-predominant Hodgkin lymphoma presented prolonged contact times (mean 6.1 min). Short contact times in mantle cell lymphoma were associated with the largest spatial displacement of PD1-positive cells (mean 12.3 µm). Although PD1-positive T cells in nodular lymphocyte-predominant Hodgkin lymphoma were fast, they remained in close contact with the lymphoma cells, in line with a dynamic immunological synapse. This pilot study shows for the first time systematic differences in the dynamic behavior of lymphoma and bystander cells between different lymphoma types.

Insertion of plastidic ß-barrel proteins into the outer envelopes of plastids involves an intermembrane space intermediate formed with Toc75-V/OEP80.

The insertion of organellar membrane proteins with the correct topology requires the following: First, the proteins must contain topogenic signals for translocation across and insertion into the membrane. Second, proteinaceous complexes in the cytoplasm, membrane, and lumen of organelles are required to drive this process. Many complexes required for the intracellular distribution of membrane proteins have been described, but the signals and components required for the insertion of plastidic ß-barrel-type proteins into the outer membrane are largely unknown. The discovery of common principles is difficult, as only a few plastidic ß-barrel proteins exist. Here, we provide evidence that the plastidic outer envelope ß-barrel proteins OEP21, OEP24, and OEP37 from pea (Pisum sativum) and Arabidopsis thaliana contain information defining the topology of the protein. The information required for the translocation of pea proteins across the outer envelope membrane is present within the six N-terminal ß-strands. This process requires the action of translocon of the outer chloroplast (TOC) membrane. After translocation into the intermembrane space, ß-barrel proteins interact with TOC75-V, as exemplified by OEP37 and P39, and are integrated into the membrane. The membrane insertion of plastidic ß-barrel proteins is affected by mutation of the last ß-strand, suggesting that this strand contributes to the insertion signal. These findings shed light on the elements and complexes involved in plastidic ß-barrel protein import.

Quantitative assessment of optical clearing methods on formalin-fixed human lymphoid tissue.

Rising interest in three-dimensional volume imaging of biological tissues for diagnostic and research purposes, calls for appropriate optical clearing methods as an indispensable requirement for high-resolution imaging on a cellular level. In recent years, many clearing protocols have emerged, though most of them focus on murine central nervous tissue. Peripheral organs or tissues of human origin have only been investigated sparsely. Therefore, we tested eight established clearing methods (BABB, Ce3D, CUBIC, ECi, ChemScale, ChemScaleQQ5, SeeDB2 and PACT) on formaldehyde-fixed human tonsils. This application-oriented taxonomy can help researchers restrict the space of their survey on clearing techniques for lymphatic tissue as it provides information on each method in regard to its efficacy, clearing speed, preservation of fluorescence labelling, toxicity, expenditure and monetary costs. We found that all of the applied clearing protocols could render the sample tissues transparent. Ce3D and PACT achieved the highest degrees of tissue transparency. Since it requires less preparing and processing time and is lower in toxicity, we recommend Ce3D for the clearing of human lymphoid tissue.

Landscape of T Follicular Helper Cell Dynamics in Human Germinal Centers.

T follicular helper (Tfh) cells play a very important role in mounting a humoral response. Studies conducted in mouse models have revealed with good kinetic and spatial resolution the dynamics of these cells in germinal centers (GC) and their cross-talk with B cells upon an immune response. However, whether a similar migratory behavior is performed by human Tfh cells is unclear, as technology to track them in situ has been lacking. In this study, we combined traditional immunohistochemistry and real-time fluorescent imaging approaches on fresh human adenoid slices to provide static and dynamic information on Tfh cells. Our data indicate that GC light zones are composed of two distinct areas in terms of Tfh cell distribution and migration. In the outer GC light zones, Tfh cells migrate actively and with a high ability to form dynamic clusters showing intense and rapid reorganization. In these outer regions, Tfh cells demonstrate multiple interactions between each other. Conversely, in central regions of GC light zones, Tfh cells are much more static, forming long-lasting conjugates. These findings reveal for the first time, to our knowledge, the dynamic behavior whereby Tfh cells migrate in human GC and highlight the heterogeneity of GC for Tfh cell motility.

Migration Properties Distinguish Tumor Cells of Classical Hodgkin Lymphoma from Anaplastic Large Cell Lymphoma Cells.

Anaplastic large cell lymphoma (ALCL) and classical Hodgkin lymphoma (cHL) are lymphomas that contain CD30-expressing tumor cells and have numerous pathological similarities. Whereas ALCL is usually diagnosed at an advanced stage, cHL more frequently presents with localized disease. The aim of the present study was to elucidate the mechanisms underlying the different clinical presentation of ALCL and cHL. Chemokine and chemokine receptor expression were similar in primary ALCL and cHL cases apart from the known overexpression of the chemokines CCL17 and CCL22 in the Hodgkin and Reed-Sternberg (HRS) cells of cHL. Consistent with the overexpression of these chemokines, primary cHL cases encountered a significantly denser T cell microenvironment than ALCL. Additionally to differences in the interaction with their microenvironment, cHL cell lines presented a lower and less efficient intrinsic cell motility than ALCL cell lines, as assessed by time-lapse microscopy in a collagen gel and transwell migration assays. We thus propose that the combination of impaired basal cell motility and differences in the interaction with the microenvironment hamper the dissemination of HRS cells in cHL when compared with the tumor cells of ALCL.

The plastid outer membrane localized LPTD1 is important for glycerolipid remodelling under phosphate starvation.

Glycerolipid synthesis in plants is coordinated between plastids and the endoplasmic reticulum (ER). A central step within the glycerolipid synthesis is the transport of phosphatidic acid from ER to chloroplasts. The chloroplast outer envelope protein TGD4 belongs to the LptD family conserved in bacteria and plants and selectively binds and may transport phosphatidic acid. We describe a second LptD-family protein in A. thaliana (atLPTD1; At2g44640) characterized by a barrel domain with an amino-acid signature typical for cyanobacterial LptDs. It forms a cation selective channel in vitro with a diameter of about 9 Å. atLPTD1 levels are induced under phosphate starvation. Plants expressing an RNAi construct against atLPTD1 show a growth phenotype under normal conditions. Expressing the RNAi against atLPTD1 in the tgd4-1 background renders the plants more sensitive to light stress or phosphate limitation than the individual mutants. Moreover, lipid analysis revealed that digalactosyldiacylglycerol and sulfoquinovosyldiacylglycerol levels remain constant in the RNAi mutants under phosphate starvation, while these two lipids are enhanced in wild-type. Based on our results, we propose a function of atLPTD1 in the transport of lipids from ER to chloroplast under phosphate starvation, which is combinatory with the function of TGD4.

Chloroplast outer envelope protein P39 in Arabidopsis thaliana belongs to the Omp85 protein family.

Proteins of the Omp85 family chaperone the membrane insertion of ß-barrel-shaped outer membrane proteins in bacteria, mitochondria, and probably chloroplasts and facilitate the transfer of nuclear-encoded cytosolically synthesized preproteins across the outer envelope of chloroplasts. This protein family is characterized by N-terminal polypeptide transport-associated (POTRA) domains and a C-terminal membrane-embedded ß-barrel. We have investigated a recently identified Omp85 family member of Arabidopsis thaliana annotated as P39. We show by in vitro and in vivo experiments that P39 is localized in chloroplasts. The electrophysiological properties of P39 are consistent with those of other Omp85 family members confirming the sequence based assignment of P39 to this family. Bioinformatic analysis showed that P39 lacks any POTRA domain, while a complete 16 stranded ß-barrel including the highly conserved L6 loop is proposed. The electrophysiological properties are most comparable to Toc75-V, which is consistent with the phylogenetic clustering of P39 in the Toc75-V rather than the Toc75-III branch of the Omp85 family tree. Taken together P39 forms a pore with Omp85 family protein characteristics. The bioinformatic comparison of the pore region of Toc75-III, Toc75-V, and P39 shows distinctions of the barrel region most likely related to function. Proteins 2017; 85:1391-1401. © 2014 Wiley Periodicals, Inc.

Separating mitochondrial protein assembly and endoplasmic reticulum tethering by selective coupling of Mdm10.

The endoplasmic reticulum-mitochondria encounter structure (ERMES) connects the mitochondrial outer membrane with the ER. Multiple functions have been linked to ERMES, including maintenance of mitochondrial morphology, protein assembly and phospholipid homeostasis. Since the mitochondrial distribution and morphology protein Mdm10 is present in both ERMES and the mitochondrial sorting and assembly machinery (SAM), it is unknown how the ERMES functions are connected on a molecular level. Here we report that conserved surface areas on opposite sides of the Mdm10 ß-barrel interact with SAM and ERMES, respectively. We generated point mutants to separate protein assembly (SAM) from morphology and phospholipid homeostasis (ERMES). Our study reveals that the ß-barrel channel of Mdm10 serves different functions. Mdm10 promotes the biogenesis of α-helical and ß-barrel proteins at SAM and functions as integral membrane anchor of ERMES, demonstrating that SAM-mediated protein assembly is distinct from ER-mitochondria contact sites.