Nikolay Samusik

Mass cytometric functional profiling of acute myeloid leukemia defines cell cycle and immunophenotypic properties that correlate with known responses to therapy

UNLABELLED Acute myeloid leukemia (AML) is characterized by a high relapse rate that has been attributed to the quiescence of leukemia stem cells (LSC), which renders them resistant to chemotherapy. However, this hypothesis is largely supported by indirect evidence and fails to explain the large differences in relapse rates across AML subtypes. To address this, bone marrow aspirates from 41 AML patients and five healthy donors were analyzed by high-dimensional mass cytometry. All patients displayed immunophenotypic and intracellular signaling abnormalities within CD34(+)CD38(lo) populations, and several karyotype- and genotype-specific surface marker patterns were identified. The immunophenotypic stem and early progenitor cell populations from patients with clinically favorable core-binding factor AML demonstrated a 5-fold higher fraction of cells in S-phase compared with other AML samples. Conversely, LSCs in less clinically favorable FLT3-ITD AML exhibited dramatic reductions in S-phase fraction. Mass cytometry also allowed direct observation of the in vivo effects of cytotoxic chemotherapy. SIGNIFICANCE The mechanisms underlying differences in relapse rates across AML subtypes are poorly understood. This study suggests that known chemotherapy sensitivities of common AML subsets are mediated by cell-cycle differences among LSCs and provides a basis for using in vivo functional characterization of AML cells to inform therapy selection.

Single-cell mass cytometry reveals distinct populations of brain myeloid cells in mouse neuroinflammation and neurodegeneration models

Neuroinflammation and neurodegeneration may represent two poles of brain pathology. Brain myeloid cells, particularly microglia, play key roles in these conditions. We employed single-cell mass cytometry (CyTOF) to compare myeloid cell populations in the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis, the R6/2 model of Huntington’s disease (HD) and the mutant superoxide dismutase 1 (mSOD1) model of amyotrophic lateral sclerosis (ALS). We identified three myeloid cell populations exclusive to the CNS and present in each disease model. Blood-derived monocytes comprised five populations and migrated to the brain in EAE, but not in HD and ALS models. Single-cell analysis resolved differences in signaling and cytokine production within similar myeloid populations in EAE compared to HD and ALS models. Moreover, these analyses highlighted α5 integrin on myeloid cells as a potential therapeutic target for neuroinflammation. Together, these findings illustrate how neuropathology may differ between inflammatory and degenerative brain disease.Myeloid cells are critical in the pathology of inflammatory and degenerative brain diseases. The authors use single-cell mass cytometry (CyTOF) to reveal distinct characteristics in these cells in models of neural inflammation and degeneration.

Three-dimensional intact-tissue sequencing of single-cell transcriptional states

Transcriptome mapping in the 3D brain RNA sequencing samples the entire transcriptome but lacks anatomical information. In situ hybridization, on the other hand, can only profile a small number of transcripts. In situ sequencing technologies address these shortcomings but face a challenge in dense, complex tissue environments. Wang et al. combined an efficient sequencing approach with hydrogel-tissue chemistry to develop a multidisciplinary technology for three-dimensional (3D) intact-tissue RNA sequencing (see the Perspective by Knöpfel). More than 1000 genes were simultaneously mapped in sections of mouse brain at single-cell resolution to define cell types and circuit states and to reveal cell organization principles. Science, this issue p. eaat5691; see also p. 328 Wang et al. describe the development and application of an RNA sequencing technology to define cell types and circuit states in the mouse brain. INTRODUCTION Single-cell RNA sequencing has demonstrated that both stable cell types and transient cell states can be discovered and defined by transcriptomes. In situ transcriptomic methods can map both RNA quantity and position; however, it remains challenging to simultaneously satisfy key technological requirements such as efficiency, signal intensity, accuracy, scalability to large gene numbers, and applicability to three-dimensional (3D) volumes. Well-established single-molecule fluorescence in situ hybridization (FISH) approaches (such as MERFISH and seqFISH) have high detection efficiency but require long RNA species (more than 1000 nucelotides) and yield lower intensity than that of enzymatic amplification methods (tens versus thousands of fluorophores per RNA molecule). Other pioneering in situ sequencing methods (via padlock probes and fluorescent in situ sequencing) use enzymatic amplification, thus achieving high intensity but with room to improve on efficiency. RATIONALE We have developed, validated, and applied STARmap (spatially-resolved transcript amplicon readout mapping). STARmap begins with labeling of cellular RNAs by pairs of DNA probes followed by enzymatic amplification so as to produce a DNA nanoball (amplicon), which eliminates background caused by mislabeling of single probes. Tissue can then be transformed into a 3D hydrogel DNA chip by anchoring DNA amplicons via an in situ–synthesized polymer network and removing proteins and lipids. This form of hydrogel-tissue chemistry replots amplicons onto an optically transparent hydrogel coordinate system; then, to identify and quantify RNA species-abundance manifested by DNA amplicons, the identity of each species is encoded as a five-base barcode and read out by means of an in situ sequencing method that decodes DNA sequence in multicolor fluorescence. Using a new two-base sequencing scheme (SEDAL), STARmap was found to simultaneously detect more than 1000 genes over six imaging cycles, in which sequencing errors in any cycle cause misdecoding and are effectively rejected. RESULTS We began by (i) detecting and quantifying a focused 160-gene set (including cell type markers and activity-regulated genes) simultaneously in mouse primary visual cortex; (ii) clustering resulting per-cell gene expression patterns into a dozen distinct inhibitory, excitatory, and non-neuronal cell types; and (iii) mapping the spatial distribution of all of these cell types across layers of cortex. For validation, per-cell-type gene expression was found to correlate well both with in situ hybridization results and with single-cell RNA sequencing, and widespread up-regulation of activity-regulated genes was observed in response to visual stimulation. We next applied STARmap to a higher cognitive area (the medial prefrontal cortex) and discovered a more complex distribution of cell types. Last, we extended STARmap to much larger numbers of genes and spatial scales; we measured 1020 genes simultaneously in sections—obtaining results concordant with the 160-gene set—and measured 28 genes across millimeter-scale volumes encompassing ~30,000 cells, revealing 3D patterning principles that jointly characterize a broad and diverse spectrum of cell types. CONCLUSION STARmap combines hydrogel-tissue chemistry and in situ DNA sequencing to achieve intact-tissue single-cell measurement of expression of more than a thousand genes. In the future, combining this intact-system gene expression measurement with complementary cellular-resolution methodologies (with which STARmap is designed to be compatible)—including in vivo activity recording, optogenetic causal tests, and anatomical connectivity in the same cells—will help bridge molecular, cellular, and circuit scales of neuroscience. STARmap for 3D transcriptome imaging and molecular cell typing. STARmap is an in situ RNA-sequencing technology that transforms intact tissue into a 3D hydrogel-tissue hybrid and measures spatially resolved single-cell transcriptomes in situ. Error- and background-reduction mechanisms are implemented at multiple layers, enabling precise RNA quantification, spatially resolved cell typing, scalability to large gene numbers, and 3D mapping of tissue architecture. Retrieving high-content gene-expression information while retaining three-dimensional (3D) positional anatomy at cellular resolution has been difficult, limiting integrative understanding of structure and function in complex biological tissues. We developed and applied a technology for 3D intact-tissue RNA sequencing, termed STARmap (spatially-resolved transcript amplicon readout mapping), which integrates hydrogel-tissue chemistry, targeted signal amplification, and in situ sequencing. The capabilities of STARmap were tested by mapping 160 to 1020 genes simultaneously in sections of mouse brain at single-cell resolution with high efficiency, accuracy, and reproducibility. Moving to thick tissue blocks, we observed a molecularly defined gradient distribution of excitatory-neuron subtypes across cubic millimeter–scale volumes (>30,000 cells) and a short-range 3D self-clustering in many inhibitory-neuron subtypes that could be identified and described with 3D STARmap.

Commonly Occurring Cell Subsets in High-Grade Serous Ovarian Tumors Identified by Single-Cell Mass Cytometry

We have performed an in-depth single-cell phenotypic characterization of high-grade serous ovarian cancer (HGSOC) by multiparametric mass cytometry (CyTOF). Using a CyTOF antibody panel to interrogate features of HGSOC biology, combined with unsupervised computational analysis, we identified noteworthy cell types co-occurring across the tumors. In addition to a dominant cell subset, each tumor harbored rarer cell phenotypes. One such group co-expressed E-cadherin and vimentin (EV), suggesting their potential role in epithelial mesenchymal transition, which was substantiated by pairwise correlation analyses. Furthermore, tumors from patients with poorer outcome had an increased frequency of another rare cell type that co-expressed vimentin, HE4, and cMyc. These poorer-outcome tumors also populated more cell phenotypes, as quantified by Simpsons diversity index. Thus, despite the recognized genomic complexity of the disease, the specific cell phenotypes uncovered here offer a focus for therapeutic intervention and disease monitoring.

Diabetes-linked transcription factor HNF4α regulates metabolism of endogenous methylarginines and β-aminoisobutyric acid by controlling expression of alanine-glyoxylate aminotransferase 2

Elevated levels of circulating asymmetric and symmetric dimethylarginines (ADMA and SDMA) predict and potentially contribute to end organ damage in cardiovascular diseases. Alanine-glyoxylate aminotransferase 2 (AGXT2) regulates systemic levels of ADMA and SDMA, and also of beta-aminoisobutyric acid (BAIB)-a modulator of lipid metabolism. We identified a putative binding site for hepatic nuclear factor 4 α (HNF4α) in AGXT2 promoter sequence. In a luciferase reporter assay we found a 75% decrease in activity of Agxt2 core promoter after disruption of the HNF4α binding site. Direct binding of HNF4α to Agxt2 promoter was confirmed by chromatin immunoprecipitation assay. siRNA-mediated knockdown of Hnf4a led to an almost 50% reduction in Agxt2 mRNA levels in Hepa 1–6 cells. Liver-specific Hnf4a knockout mice exhibited a 90% decrease in liver Agxt2 expression and activity, and elevated plasma levels of ADMA, SDMA and BAIB, compared to wild-type littermates. Thus we identified HNF4α as a major regulator of Agxt2 expression. Considering a strong association between human HNF4A polymorphisms and increased risk of type 2 diabetes our current findings suggest that downregulation of AGXT2 and subsequent impairment in metabolism of dimethylarginines and BAIB caused by HNF4α deficiency might contribute to development of cardiovascular complications in diabetic patients.

Publisher Correction: High-resolution myogenic lineage mapping by single-cell mass cytometry

In the version of this Article originally published, the name of author Andrew Tri Van Ho was coded wrongly, resulting in it being incorrect when exported to citation databases. This has been corrected, though no visible changes will be apparent.

Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging

Summary A highly multiplexed cytometric imaging approach, termed co-detection by indexing (CODEX), is used here to create multiplexed datasets of normal and lupus (MRL/lpr) murine spleens. CODEX iteratively visualizes antibody binding events using DNA barcodes, fluorescent dNTP analogs, and an in situ polymerization-based indexing procedure. An algorithmic pipeline for single-cell antigen quantification in tightly packed tissues was developed and used to overlay well-known morphological features with de novo characterization of lymphoid tissue architecture at a single-cell and cellular neighborhood levels. We observed an unexpected, profound impact of the cellular neighborhood on the expression of protein receptors on immune cells. By comparing normal murine spleen to spleens from animals with systemic autoimmune disease (MRL/lpr), extensive and previously uncharacterized splenic cell-interaction dynamics in the healthy versus diseased state was observed. The fidelity of multiplexed spatial cytometry demonstrated here allows for quantitative systemic characterization of tissue architecture in normal and clinically aberrant samples.

Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks

Mass cytometry (CyTOF) has greatly expanded the capability of cytometry. It is now easy to generate multiple CyTOF samples in a single study, with each sample containing single-cell measurement on 50 markers for more than hundreds of thousands of cells. Current methods do not adequately address the issues concerning combining multiple samples for subpopulation discovery, and these issues can be quickly and dramatically amplified with increasing number of samples. To overcome this limitation, we developed Partition-Assisted Clustering and Multiple Alignments of Networks (PAC-MAN) for the fast automatic identification of cell populations in CyTOF data closely matching that of expert manual-discovery, and for alignments between subpopulations across samples to define dataset-level cellular states. PAC-MAN is computationally efficient, allowing the management of very large CyTOF datasets, which are increasingly common in clinical studies and cancer studies that monitor various tissue samples for each subject.

Abstract A089: Multiparametric immunofluorescence analysis of the tumor microenvironment using CODEX

The tumor microenvironment plays a critical role in cancer progression and has implications for the efficacy of various cancer immunotherapy treatment options. Immune infiltrates within the tumor microenvironment can correlate with both positive and negative outcomes, depending upon the both the type of cancer as well as infiltrating immune cell(s). These analyses are typically performed using standard immunofluorescence and immunohistochemistry assays where no more than four simultaneous parameters can be visualized on the same tissue. Unfortunately, these tools cannot fully characterize the complexity of the tumor microenvironment due to the inherent limitations of fluorophore spectral overlap. In order to identify each type of immune and tumor cell within a single tissue, at least 40 parameters need to be measured simultaneously. We have developed a multiparametric immunofluorescence technology, entitled CODEX (Co-Detection by IndEXing), which utilizes unique DNA tags as a means of iteratively measuring more than 40 parameters within the same tissue. More than 40 human antibodies have been validated using this approach, including numerous immune markers, checkpoint ligands, tumor markers and cellular activity markers. We are currently analyzing tissue sample from patients with lung cancer. By measuring nearly 50 simultaneous markers within the same tissue, CODEX has the potential to greatly enhance our knowledge of the tumor microenvironment and more accurately define immune infiltrates at the single-cell level. Citation Format: Julia Kennedy-Darling, Garry P. Nolan, Yury Goltsev, Nikolay Samusik. Multiparametric immunofluorescence analysis of the tumor microenvironment using CODEX [abstract]. In: Proceedings of the Second CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; 2016 Sept 25-28; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(11 Suppl):Abstract nr A089.

Continuous visualization of differences between biological conditions in single-cell data

In high-dimensional single cell data, comparing changes in functional markers between conditions is typically done across manual or algorithm-derived partitions based on population-defining markers. Visualizations of these partitions is commonly done on low-dimensional embeddings (eg. t-SNE), colored by per-partition changes. Here, we provide an analysis and visualization tool that performs these comparisons across overlapping k-nearest neighbor (KNN) groupings. This allows one to color low-dimensional embeddings by marker changes without hard boundaries imposed by partitioning. We devised an objective optimization of k based on minimizing functional marker KNN imputation error. Proof-of-concept work visualized the exact location of an IL-7 responsive subset in a B cell developmental trajectory on a t-SNE map independent of clustering. Per-condition cell frequency analysis revealed that KNN is sensitive to detecting artifacts due to marker shift, and therefore can also be valuable in a quality control pipeline. Overall, we found that KNN groupings lead to useful multiple condition visualizations and efficiently extract a large amount of information from mass cytometry data. Our software is publicly available through the Bioconductor package Sconify.