Computational Approaches for Connecting Maternal Stress to Preterm Birth.

Multiple studies have hinted at a complex connection between maternal stress and preterm birth (PTB). This article describes the potential of computational methods to provide new insights into this relationship. For this, we outline existing approaches for stress assessments and various data modalities available for profiling stress responses, and review studies that sought either to establish a connection between stress and PTB or to predict PTB based on stress-related factors. Finally, we summarize the challenges of computational methods, highlighting potential future research directions within this field.

Imaging-based deep learning in kidney diseases: recent progress and future prospects.

Kidney diseases result from various causes, which can generally be divided into neoplastic and non-neoplastic diseases. Deep learning based on medical imaging is an established methodology for further data mining and an evolving field of expertise, which provides the possibility for precise management of kidney diseases. Recently, imaging-based deep learning has been widely applied to many clinical scenarios of kidney diseases including organ segmentation, lesion detection, differential diagnosis, surgical planning, and prognosis prediction, which can provide support for disease diagnosis and management. In this review, we will introduce the basic methodology of imaging-based deep learning and its recent clinical applications in neoplastic and non-neoplastic kidney diseases. Additionally, we further discuss its current challenges and future prospects and conclude that achieving data balance, addressing heterogeneity, and managing data size remain challenges for imaging-based deep learning. Meanwhile, the interpretability of algorithms, ethical risks, and barriers of bias assessment are also issues that require consideration in future development. We hope to provide urologists, nephrologists, and radiologists with clear ideas about imaging-based deep learning and reveal its great potential in clinical practice.Critical relevance statement The wide clinical applications of imaging-based deep learning in kidney diseases can help doctors to diagnose, treat, and manage patients with neoplastic or non-neoplastic renal diseases.Key points• Imaging-based deep learning is widely applied to neoplastic and non-neoplastic renal diseases.• Imaging-based deep learning improves the accuracy of the delineation, diagnosis, and evaluation of kidney diseases.• The small dataset, various lesion sizes, and so on are still challenges for deep learning.

Clinical Applications and Recent Updates of Simultaneous Multi-slice Technique in Accelerated MRI.

Simultaneous multi-slice (SMS) is a magnetic resonance imaging (MRI) acceleration technique that utilizes multi-band radio-frequency pulses to simultaneously excite and encode multiple slices. Currently, SMS has been widely studied and applied in the MRI examination to reduce acquisition time, which can significantly improve the examination efficiency and patient throughput. Moreover, SMS technique can improve spatial resolution, which is of great value in disease diagnosis, treatment response monitoring, and prognosis prediction. This review will briefly introduce the technical principles of SMS, and summarize its current clinical applications. More importantly, we will discuss the recent technical progress and future research direction of SMS, hoping to highlight the clinical value and scientific potential of this technique.

T cell-mediated curation and restructuring of tumor tissue coordinates an effective immune response.

Antigen-specific T cells traffic to, are influenced by, and create unique cellular microenvironments. Here we characterize these microenvironments over time with multiplexed imaging in a melanoma model of adoptive T cell therapy and human patients with melanoma treated with checkpoint inhibitor therapy. Multicellular neighborhood analysis reveals dynamic immune cell infiltration and inflamed tumor cell neighborhoods associated with CD8+ T cells. T cell-focused analysis indicates T cells are found along a continuum of neighborhoods that reflect the progressive steps coordinating the anti-tumor immune response. More effective anti-tumor immune responses are characterized by inflamed tumor-T cell neighborhoods, flanked by dense immune infiltration neighborhoods. Conversely, ineffective T cell therapies express anti-inflammatory cytokines, resulting in regulatory neighborhoods, spatially disrupting productive T cell-immune and -tumor interactions. Our study provides in situ mechanistic insights into temporal tumor microenvironment changes, cell interactions critical for response, and spatial correlates of immunotherapy outcomes, informing cellular therapy evaluation and engineering.

Large-scale correlation network construction for unraveling the coordination of complex biological systems.

Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.

Treatment management for BRAF-mutant melanoma patients with tumor recurrence on adjuvant therapy: a multicenter study from the prospective skin cancer registry ADOREG.

BACKGROUND: Adjuvant therapy with immune-checkpoint inhibitors (CPI) or BRAF/MEK-directed targeted therapy (TT) improves recurrence-free survival (RFS) for patients with advanced, BRAFV600-mutant (BRAFmut) resected melanoma. However, 40% of these patients will develop distant metastases (DM) within 5 years, which require systemic therapy. Little data exist to guide the choice of upfront adjuvant therapy or treatment management upon DM. This study evaluated the efficacy of subsequent treatments following tumor recurrence upon upfront adjuvant therapy. METHODS: For this multicenter cohort study, we identified 515 BRAFmut patients with resected stage III melanoma who were treated with PD-1 inhibitors (anti-PD1) or TT in the adjuvant setting. Disease characteristics, treatment regimens, details on tumor recurrence, subsequent treatment management, and survival outcomes were collected within the prospective, real-world skin cancer registry ADOReg. Primary endpoints included progression-free survival (PFS) following DM and best tumor response to first-line (1L) treatments. RESULTS: Among 515 eligible patients, 273 patients received adjuvant anti-PD1 and 242 adjuvant TT. At a median follow-up of 21 months, 54.6% of anti-PD1 patients and 36.4% of TT patients recurred, while 39.6% (anti-PD1) and 29.3% (TT) developed DM. Risk of recurrence was significantly reduced in patients treated with TT compared with anti-PD1 (adjusted HR 0.52; 95% CI 0.40 to 0.68, p<0.001). Likewise, median RFS was significantly longer in TT-treated patients (31 vs 17 months, p<0.001). Patients who received TT as second adjuvant treatment upon locoregional recurrence had a longer RFS2 as compared with adjuvant CPI (41 vs 6 months, p=0.009). Patients who recurred at distant sites following adjuvant TT showed favorable response rates (42.9%) after switching to 1L ipilimumab+nivolumab (ipi+nivo). Patients with DM during adjuvant anti-PD1 achieved response rates of 58.7% after switching to 1L TT and 35.3% for 1L ipi+nivo. Overall, median PFS was significantly longer in patients who switched treatments for stage IV disease (median PFS 9 vs 5 months, p=0.004). CONCLUSIONS: BRAFmut melanoma patients who developed DM upon upfront adjuvant therapy achieve favorable tumor control and prolonged PFS after switching treatment modalities in the first-line setting of stage IV disease. Patients with locoregional recurrence benefit from complete resection of recurrence followed by a second adjuvant treatment with TT.

Concerted epithelial and stromal changes during progression of Barrett's Esophagus to invasive adenocarcinoma exposed by multi-scale, multi-omics analysis.

Esophageal adenocarcinoma arises from Barrett's esophagus, a precancerous metaplastic replacement of squamous by columnar epithelium in response to chronic inflammation. Multi-omics profiling, integrating single-cell transcriptomics, extracellular matrix proteomics, tissue-mechanics and spatial proteomics of 64 samples from 12 patients' paths of progression from squamous epithelium through metaplasia, dysplasia to adenocarcinoma, revealed shared and patient-specific progression characteristics. The classic metaplastic replacement of epithelial cells was paralleled by metaplastic changes in stromal cells, ECM and tissue stiffness. Strikingly, this change in tissue state at metaplasia was already accompanied by appearance of fibroblasts with characteristics of carcinoma-associated fibroblasts and of an NK cell-associated immunosuppressive microenvironment. Thus, Barrett's esophagus progresses as a coordinated multi-component system, supporting treatment paradigms that go beyond targeting cancerous cells to incorporating stromal reprogramming.

Two interacting ethylene response factors negatively regulate peach resistance to Lasiodiplodia theobromae.

Gummosis is 1 of the most common and destructive diseases threatening global peach (Prunus persica) production. Our previous studies have revealed that ethylene and methyl jasmonate enhance peach susceptibility to Lasiodiplodia theobromae, a virulent pathogen inducing gummosis; however, the underlying molecular mechanisms remain obscure. Here, 2 ethylene response factors (ERFs), PpERF98 and PpERF1, were identified as negative regulators in peach response to L. theobromae infection. Expression of 2 putative paralogs, PpERF98-1/2, was dramatically induced by ethylene and L. theobromae treatments and accumulated highly in the gummosis-sensitive cultivar. Silencing of PpERF98-1/2 increased salicylic acid (SA) content and pathogenesis-related genes PpPR1 and PpPR2 transcripts, conferring peach resistance to L. theobromae, whereas peach and tomato (Solanum lycopersicum) plants overexpressing either of PpERF98-1/2 showed opposite changes. Also, jasmonic acid markedly accumulated in PpERF98-1/2-silenced plants, but reduction in PpPR3, PpPR4, and PpCHI (Chitinase) transcripts indicated a blocked signaling pathway. PpERF98-1 and 2 were further demonstrated to directly bind the promoters of 2 putative paralogous PpERF1 genes and to activate the ERF branch of the jasmonate/ethylene signaling pathway, thus attenuating SA-dependent defenses. The lesion phenotypes of peach seedlings overexpressing PpERF1-1/2 and PpERF98-1/2 were similar. Furthermore, PpERF98-1/2 formed homodimers/heterodimers and interacted with the 2 PpERF1 proteins to amplify the jasmonate/ethylene signaling pathway, as larger lesions were observed in peach plants cooverexpressing PpERF98 with PpERF1 relative to individual PpERF98 overexpression. Overall, our work deciphers an important regulatory network of ethylene-mediated peach susceptibility to L. theobromae based on a PpERF98-PpERF1 transcriptional cascade, which could be utilized as a potential target for genetic engineering to augment protection against L. theobromae-mediated diseases in crops and trees.

Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning.

Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches. Objectives: The primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions. Materials and Methods: In a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF). Results: Jointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs. Conclusions: Elucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics.

Annotation of spatially resolved single-cell data with STELLAR.

Accurate cell-type annotation from spatially resolved single cells is crucial to understand functional spatial biology that is the basis of tissue organization. However, current computational methods for annotating spatially resolved single-cell data are typically based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method for cell-type discovery and identification in spatially resolved single-cell datasets. STELLAR automatically assigns cells to cell types present in the annotated reference dataset and discovers novel cell types and cell states. STELLAR transfers annotations across different dissection regions, different tissues and different donors, and learns cell representations that capture higher-order tissue structures. We successfully applied STELLAR to CODEX multiplexed fluorescent microscopy data and multiplexed RNA imaging datasets. Within the Human BioMolecular Atlas Program, STELLAR has annotated 2.6 million spatially resolved single cells with dramatic time savings.

Naphthoquinones and triterpenoids from Arnebia euchroma (Royle) Johnst and their hypoglycemic and lipid-lowering effects.

A new pentacyclic triterpenoid, 2-hydroxy-1-ene-hydroxyhopanone (19), and a new benzoxepin-5-one, 3-(4-methyl-3-penten-1-yl)-6-hydroxy-9-methoxy-2H-1-benzoxepin-5-one (25), along with 26 known compounds (1-18, 20-24, 26-28), were isolated from the roots of Arnebia euchroma (Royle) Johnst. The structures of the new compounds were elucidated by extensive spectroscopic analyses. The absolute configurations of shikonofurans 9-13 were determined by quantum chemical ECD calculations and CD spectra comparison for the first time. Pharmacological study revealed that naphthoquinones 1-5, 7, and 8 had obvious cytotoxicity toward human lung adenocarcinoma A549 cell line. Meanwhile, the hypoglycemic and lipid-lowering effects of isolated compounds were assessed by checking their inhibitory effects on key enzymes regulating glucose and lipid metabolism. Results showed that compounds 1, 3, 5, 6, 8, 18, and 19 could inhibit the activity of ATP-citrate lyase (ACL); compound 7 could inhibit the activity of acetyl-CoA carboxylase (ACC1); while compounds 8 and 19 showed inhibitory effects on protein tyrosine phosphatase 1B (PTP1B). Among them, the naphthoquinone 6, steroid 18, and triterpenoid 19 showed moderate inhibitory effects on ACL and PTP1B, but didn't exhibit obvious cytotoxicity. This study demonstrated that compounds 6, 18, and 19 show great promising for the development of new agents for the treatment of metabolic diseases.

Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data.

Multiplexed imaging is a recently developed and powerful single-cell biology research tool. However, it presents new sources of technical noise that are distinct from other types of single-cell data, necessitating new practices for single-cell multiplexed imaging processing and analysis, particularly regarding cell-type identification. Here we created single-cell multiplexed imaging datasets by performing CODEX on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. After cell segmentation, we implemented five different normalization techniques crossed with four unsupervised clustering algorithms, resulting in 20 unique cell-type annotations for the same dataset. We generated two standard annotations: hand-gated cell types and cell types produced by over-clustering with spatial verification. We then compared these annotations at four levels of cell-type granularity. First, increasing cell-type granularity led to decreased labeling accuracy; therefore, subtle phenotype annotations should be avoided at the clustering step. Second, accuracy in cell-type identification varied more with normalization choice than with clustering algorithm. Third, unsupervised clustering better accounted for segmentation noise during cell-type annotation than hand-gating. Fourth, Z-score normalization was generally effective in mitigating the effects of noise from single-cell multiplexed imaging. Variation in cell-type identification will lead to significant differential spatial results such as cellular neighborhood analysis; consequently, we also make recommendations for accurately assigning cell-type labels to CODEX multiplexed imaging.

Gene Regulatory Network Analysis and Engineering Directs Development and Vascularization of Multilineage Human Liver Organoids.

Pluripotent stem cell (PSC)-derived organoids have emerged as novel multicellular models of human tissue development but display immature phenotypes, aberrant tissue fates, and a limited subset of cells. Here, we demonstrate that integrated analysis and engineering of gene regulatory networks (GRNs) in PSC-derived multilineage human liver organoids direct maturation and vascular morphogenesis in vitro. Overexpression of PROX1 and ATF5, combined with targeted CRISPR-based transcriptional activation of endogenous CYP3A4, reprograms tissue GRNs and improves native liver functions, such as FXR signaling, CYP3A4 enzymatic activity, and stromal cell reactivity. The engineered tissues possess superior liver identity when compared with other PSC-derived liver organoids and show the presence of hepatocyte, biliary, endothelial, and stellate-like cell populations in single-cell RNA-seq analysis. Finally, they show hepatic functions when studied in vivo. Collectively, our approach provides an experimental framework to direct organogenesis in vitro by systematically probing molecular pathways and transcriptional networks that promote tissue development.

Asymmetric Ring-Closing Aminooxygenation of Alkenes en Route to 2-Amino-1,3-Diols with Vicinal Stereocenters.

A ring-closing aminooxygenation of alkenes with N-benzoyloxycarbamates occurs with very high diastereoselectivity (typically >20:1 d.r.) and very high enantioselectivity (up to 99% ee). The reaction is catalyzed by a recently developed chiral-at-metal ruthenium complex at catalyst loadings of 0.5-1.0 mol %. The reaction is proposed to proceed through a ruthenium nitrenoid intermediate that depending on the nature of the substrate undergoes either an aminooxygenation (1,2-disubstituted alkenes) or stops at the stage of the aziridination (trisubstituted alkenes), which can then be ring opened with benzoic acid. The resulting chiral cyclic carbamates can be hydrolyzed under basic conditions to provide versatile chiral 2-amino-1,3-diols with vicinal stereocenters.

Intramolecular C(sp3 )-H Bond Oxygenation by Transition-Metal Acylnitrenoids.

This study demonstrates for the first time that easily accessible transition-metal acylnitrenoids can be used for controlled direct C(sp3 )-H oxygenations. Specifically, a ruthenium catalyst activates N-benzoyloxycarbamates as nitrene precursors towards regioselective intramolecular C-H oxygenations to provide cyclic carbonates, hydroxylated carbamates, or 1,2-diols. The method can be applied to the chemoselective C-H oxygenation of benzylic, allylic, and propargylic C(sp3 )-H bonds. The reaction can be performed in an enantioselective fashion and switched in a catalyst-controlled fashion between C-H oxygenation and C-H amination. This work provides a new reaction mode for the regiocontrolled and stereocontrolled conversion of C(sp3 )-H into C(sp3 )-O bonds.

Transcriptome Dynamics of Hematopoietic Stem Cell Formation Revealed Using a Combinatorial Runx1 and Ly6a Reporter System.

Studies of hematopoietic stem cell (HSC) development from pre-HSC-producing hemogenic endothelial cells (HECs) are hampered by the rarity of these cells and the presence of other cell types with overlapping marker expression profiles. We generated a Tg(Runx1-mKO2; Ly6a-GFP) dual reporter mouse to visualize hematopoietic commitment and study pre-HSC emergence and maturation. Runx1-mKO2 marked all intra-arterial HECs and hematopoietic cluster cells (HCCs), including pre-HSCs, myeloid- and lymphoid progenitors, and HSCs themselves. However, HSC and lymphoid potential were almost exclusively found in reporter double-positive (DP) cells. Robust HSC activity was first detected in DP cells of the placenta, reflecting the importance of this niche for (pre-)HSC maturation and expansion before the fetal liver stage. A time course analysis by single-cell RNA sequencing revealed that as pre-HSCs mature into fetal liver stage HSCs, they show signs of interferon exposure, exhibit signatures of multi-lineage differentiation gene expression, and develop a prolonged cell cycle reminiscent of quiescent adult HSCs.

Ligustrazine Prevents Intervertebral Disc Degeneration via Suppression of Aberrant TGFß Activation in Nucleus Pulposus Cells.

OBJECTIVES: Aberrant transforming growth factor ß (TGFß) activation is detrimental to both nucleus pulposus (NP) cells and cartilage endplates (CEPs), which can lead to intervertebral disc degeneration (IDD). Ligustrazine (LIG) reduces the expression of inflammatory factors and TGFß1 in hypertrophic CEP to prevent IDD. In this study, we investigate the effects of LIG on NP cells and the TGFß signaling. DESIGN: LIG was injected to the lumbar spinal instability (LSI) mouse model. The effect of LIG was evaluated by intervertebral disc (IVD) score in the LSI mouse model. The expression of activated TGFß was examined using immunostaining with pSmad2/3 antibody. The upright posture (UP) rat model was also treated and evaluated in the same manner to assess the effect of LIG. In ex vivo study, IVDs from four-week old mice were isolated and treated with 10-5, 10-6, and 10-7 M of LIG. We used western blot to detect activated TGFß expression. TGFß-treated human nucleus pulposus cells (HNPCs) were cotreated with optimized dose of LIG in vitro. Immunofluorescence staining was performed to determine pSmad2/3, connective tissue growth factor (CCN2), and aggrecan (ACAN) expression levels. RESULTS: IVD score and the percentage of pSmad2/3+ NP cells were low in LIG-treated LSI mice in comparison with LSI mice, but close to the levels in the Sham group. Similarly, LIG reduced the overexpression of TGFß1 in NP cells. The inhibitory effect of LIG was dose dependent. A dose of 10-5 M LIG not only strongly attenuated Smad2/3 phosphorylation in TGFß-treated IVD ex vivo but also suppressed pSmad2/3, CCN2, and ACAN expression in TGFß-treated NP cells in vitro. CONCLUSIONS: LIG prevents IDD via suppression of TGFß overactivation in NP cells.

Non-C2-Symmetric Chiral-at-Ruthenium Catalyst for Highly Efficient Enantioselective Intramolecular C(sp3)-H Amidation.

A new class of chiral ruthenium catalysts is introduced in which ruthenium is cyclometalated by two 7-methyl-1,7-phenanthrolinium heterocycles, resulting in chelating pyridylidene remote N-heterocyclic carbene ligands (rNHCs). The overall chirality results from a stereogenic metal center featuring either a Λ or Δ absolute configuration. This work features the importance of the relative metal-centered stereochemistry. Only the non-C2-symmetric chiral-at-ruthenium complexes display unprecedented catalytic activity for the intramolecular C(sp3)-H amidation of 1,4,2-dioxazol-5-ones to provide chiral γ-lactams with up to 99:1 er and catalyst loadings down to 0.005 mol % (up to 11 200 TON), while the C2-symmetric diastereomer favors an undesired Curtius-type rearrangement. DFT calculations elucidate the origins of the superior C-H amidation reactivity displayed by the non-C2-symmetric catalysts compared to related C2-symmetric counterparts.

SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species.

Single-cell RNA-seq has emerged as a powerful tool in diverse applications, from determining the cell-type composition of tissues to uncovering regulators of developmental programs. A near-universal step in the analysis of single-cell RNA-seq data is to hypothesize the identity of each cell. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single-cell RNA-seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single-cell RNA-seq data in comparison to reference single-cell RNA-seq data. SingleCellNet compares favorably to other methods in sensitivity and specificity, and it is able to classify across platforms and species. We highlight SingleCellNet's utility by classifying previously undetermined cells, and by assessing the outcome of a cell fate engineering experiment.

SCD1 and SCD2 Form a Complex That Functions with the Exocyst and RabE1 in Exocytosis and Cytokinesis.

Although exocytosis is critical for the proper trafficking of materials to the plasma membrane, relatively little is known about the mechanistic details of post-Golgi trafficking in plants. Here, we demonstrate that the DENN (Differentially Expressed in Normal and Neoplastic cells) domain protein STOMATAL CYTOKINESIS DEFECTIVE1 (SCD1) and SCD2 form a previously unknown protein complex, the SCD complex, that functionally interacts with subunits of the exocyst complex and the RabE1 family of GTPases in Arabidopsis thaliana Consistent with a role in post-Golgi trafficking, scd1 and scd2 mutants display defects in exocytosis and recycling of PIN2-GFP. Perturbation of exocytosis using the small molecule Endosidin2 results in growth inhibition and PIN2-GFP trafficking defects in scd1 and scd2 mutants. In addition to the exocyst, the SCD complex binds in a nucleotide state-specific manner with Sec4p/Rab8-related RabE1 GTPases and overexpression of wild-type RabE1 rescues scd1 temperature-sensitive mutants. Furthermore, SCD1 colocalizes with the exocyst subunit, SEC15B, and RabE1 at the cell plate and in distinct punctae at or near the plasma membrane. Our findings reveal a mechanism for plant exocytosis, through the identification and characterization of a protein interaction network that includes the SCD complex, RabE1, and the exocyst.