CODEX multiplexed tissue imaging with DNA-conjugated antibodies

Black, Sarah; Phillips, Darci; Hickey, John W.; Kennedy‐Darling, Julia; Venkataraaman, Vishal G.; Samusik, Nikolay; Goltsev, Yury; Schürch, Christian M.; Nolan, Garry P.

doi:10.1038/s41596-021-00556-8

Cited by 271 publications

(264 citation statements)

References 39 publications

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“…CODEX multiplexed imaging was done using a CODEX staining and imaging protocol previously described in detail (16,19). Settings used for the microscope are listed in Supplemental Table 1.…”

Section: Codex Imagingmentioning

confidence: 99%

“…For this analysis we used imaging data collected from four tissue blocks from the same human donor from the transverse, ascending, descending, and sigmoid colon (regions 1-4, respectively) made into a single array. We used a 47 oligonucleotide-barcoded antibody panel to image with the CODEX technology (16,19), which involves cyclic stripping, annealing, and imaging of fluorescently labeled oligonucleotides complementary to the oligonucleotides that barcode the antibodies used in staining (Figure 1A).…”

Section: Codex Multiplexed Imaging Of the Human Colonmentioning

confidence: 99%

“…Incorrect celltype identification will lead to false interpretations of spatial features and study conclusions. Most studies using multiplexed imaging technologies have employed previously established pipelines created for non-imaging-based, single-cell-type identification, such as hand-gating flow plots or unsupervised clustering, and have used various methods of raw data processing and normalization (10,(15)(16)(17)(18)(19)(20).…”

Section: Introductionmentioning

confidence: 99%

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Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data

et al. 2021

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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.

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“…CODEX multiplexed imaging was done using a CODEX staining and imaging protocol previously described in detail (16,19). Settings used for the microscope are listed in Supplemental Table 1.…”

Section: Codex Imagingmentioning

confidence: 99%

Section: Codex Multiplexed Imaging Of the Human Colonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data

et al. 2021

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“…Several technologies have been developed to uncover spatial resolutions of cell types, which are a great resource to resolve immunological host responses to solid tumors [93]. Co-detection by indexing (CODEX), which uses DNA-conjugated antibodies, allows simultaneous overview of up to 60 markers in situ [94]. However, these data may need to be captured, stored, and analyzed in combination.…”

Section: Spatial Multiplex Analysesmentioning

confidence: 99%

Shifting Gears in Precision Oncology—Challenges and Opportunities of Integrative Data Analysis

2021

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For decades, research relating to modification of host immunity towards antitumor response activation has been ongoing, with the breakthrough discovery of immune-checkpoint blockers. Several biomarkers with potential predictive value have been reported in recent studies for these novel therapies. However, with the plethora of therapeutic options existing for a given cancer entity, modern oncology is now being confronted with multifactorial interpretation to devise “the best therapy” for the individual patient. Into the bargain come the multiverse guidelines for established and emerging diagnostic biomarkers, as well as the complex interplay between cancer cells and tumor microenvironment, provoking immense challenges in the therapy decision-making process. Through this review, we present various molecular diagnostic modalities and techniques, such as genomics, immunohistochemistry and quantitative image analysis, which have the potential of becoming powerful tools in the development of an optimal treatment regime when analogized with patient characteristics. We will summarize the underlying complexities of these methods and shed light upon the necessary considerations and requirements for data integration. It is our hope to provide compelling evidence to emphasize on the need for inclusion of integrative data analysis in modern cancer therapy, and thereupon paving a path towards precision medicine and better patient outcomes.

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“…Imaging of fluorescently labeled biological materials underpins a wide range of state of the art techniques [1][2][3][4][5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

PySeq2500: An open source toolkit for repurposing HiSeq 2500 sequencing systems as versatile fluidics and imaging platforms

Pandit

Petrescu

Cuevas

et al. 2021

Preprint

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Fluorescence microscopy is a key method in the life sciences. State of the art -omics methods combine fluorescence microscopy with complex protocols to visualize tens to thousands of features in each of millions of pixels across samples. These -omics methods require precise control of temperature, reagent application, and image acquisition parameters during iterative chemistry and imaging cycles conducted over the course of days or weeks. Automated execution of such methods enables robust and reproducible data generation. However, few commercial solutions exist for temperature controlled, fluidics coupled fluorescence imaging, and implementation of bespoke instrumentation requires specialized engineering expertise. Here we present PySeq2500, an open source Python code base and flow cell design that converts the Illumina HiSeq 2500 instrument into an open platform for programmable applications. Customizable PySeq2500 protocols enable experimental designs involving simultaneous 4-channel image acquisition, temperature control, reagent exchange, stable positioning, and sample integrity over extended experiments. To demonstrate accessible automation of complex, multi-day workflows, we use the PySeq2500 system for unattended execution of iterative indirect immunofluorescence imaging (4i). Our automated 4i method uses off-the-shelf antibodies over multiple cycles of staining, imaging, and antibody elution to build highly multiplexed maps of cell types and pathological features in mouse and postmortem human spinal cord sections. As demonstrated here, PySeq2500 enables non-specialists to develop and implement state of the art fluidics coupled imaging methods in a widely available benchtop system.

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CODEX multiplexed tissue imaging with DNA-conjugated antibodies

Cited by 271 publications

References 39 publications

Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data

Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data

Shifting Gears in Precision Oncology—Challenges and Opportunities of Integrative Data Analysis

PySeq2500: An open source toolkit for repurposing HiSeq 2500 sequencing systems as versatile fluidics and imaging platforms

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