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Signal removal methods for highly multiplexed immunofluorescent staining using antibody conjugated oligonucleotides

机译:使用抗体偶联的寡核苷酸进行高度多重免疫荧光染色的信号去除方法

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Successful cancer treatment continues to elude modern medicine and its arsenal of therapeutic strategies.Therapy resistance is driven by significant tumor heterogeneity, complex interactions between malignant,microenvironmental and immune cells and cross talk between signaling pathways. Advances in molecularcharacterization technologies such as next generation sequencing have helped unravel this network of interactions andhave vastly affected how cancer is diagnosed and treated. However, the translation of complex genomic analyses topathological diagnosis remains challenging using conventional immunofluorescence (IF) staining, which is typicallylimited to 2-5 antigens. Numerous strategies to increase distinct antigen detection on a single sample have beeninvestigated, but all have deleterious effects on the tissue limiting the maximum number of biomarkers that can beimaged on a single sample and none can be seamlessly integrated into routine clinical workflows. To facilitate readyintegration into clinical histopathology, we have developed a novel cyclic IF (cycIF) technology based on antibodyconjugated oligonucleotides (Ab-oligos). In situ hybridization of complementary oligonucleotides (oligos) facilitatesbiomarker labeling for imaging on any conventional fluorescent microscope. We have validated a variety of oligoconfigurations and their respective signal removal strategies capable of diminishing fluorescent signal to levels ofautofluorescence before subsequent staining cycles. Robust signal removal is performed without the employment ofharsh conditions or reagents, maintaining tissue integrity and antigenicity for higher dimensionality immunostaining of asingle sample. Our platform Ab-oligo cycIF technology uses conventional fluorophores and microscopes, allowing fordissemination to a broad audience and congruent integration into clinical histopathology workflows.
机译:成功的癌症治疗继续被现代医学及其治疗策略所忽视。\ r \ n治疗阻力由显着的肿瘤异质性,恶性肿瘤,\ r \ n \ n微环境和免疫细胞之间复杂的相互作用以及信号通路之间的串扰驱动。分子表征技术的进步,例如下一代测序,已经帮助阐明了这种相互作用网络,并且已经极大地影响了癌症的诊断和治疗方式。但是,使用常规的免疫荧光(IF)染色将复杂的基因组分析转换为病理诊断仍然具有挑战性,通常仅限于2-5个抗原。已经研究了许多增加单个样品上独特抗原检测的策略,但是所有策略均对组织产生有害影响,限制了单个样品上可以成像的最大生物标志物数量,并且无法无缝整合到其中。常规临床工作流程。为了促进准备就绪/整合到临床组织病理学中,我们开发了一种基于抗体/偶联寡核苷酸(Ab-oligos)的新型环状IF(cycIF)技术。互补寡核苷酸(寡核苷酸)的原位杂交有助于在任何常规荧光显微镜上成像的生物标志物标记。我们已经验证了多种寡核苷酸构型及其各自的信号去除策略,它们能够在随后的染色循环之前将荧光信号降低至\ r \ n自发荧光水平。无需使用苛刻的条件或试剂即可进行稳健的信号去除,从而保持组织完整性和抗原性,从而可以对单个样品进行更高维度的免疫染色。我们的平台Ab-oligo cycIF技术使用常规的荧光团和显微镜,可广泛传播给广大观众,并与临床组织病理学工作流程完全融合。

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    Department of Biomedical Engineering Computational Biology Program, Oregon Health and Science University, Portland, OR 97201;

    Department of Biomedical Engineering Computational Biology Program, Oregon Health and Science University, Portland, OR 97201;

    Department of Biomedical Engineering Computational Biology Program, Oregon Health and Science University, Portland, OR 97201;

    Department of Biomedical Engineering Computational Biology Program, Oregon Health and Science University, Portland, OR 97201;

    Department of Biomedical Engineering Computational Biology Program, Oregon Health and Science University, Portland, OR 97201Computational Biology Program, Oregon Health and Science University, Portland, OR 97201;

    Department of Biomedical Engineering Computational Biology Program, Oregon Health and Science University, Portland, OR 97201Computational Biology Program, Oregon Health and Science University, Portland, OR 97201;

    Department of Biomedical Engineering Computational Biology Program, Oregon Health and Science University, Portland, OR 97201 OHSU Center for Spatial Systems Biomedicine, Computational Biology Program, Oregon Health and Science University, Portland, OR 97201;

    Quantitative Imaging Systems, Pittsburg, PA 15238;

    Department of Biomedical Engineering Computational Biology Program, Oregon Health and Science University, Portland, OR 97201 Knight Cancer Institute Computational Biology Program, Oregon Health and Science University, Portland, OR 97201 OHSU Center for SpatialSystems Biomedicine Computational Biology Program, Oregon Health and Science University, Portland, OR 97201;

    Department of Biomedical Engineering Computational Biology Program, Oregon Health and Science University, Portland, OR 97201 Knight Cancer Institute Computational Biology Program, Oregon Health and Science University, Portland, OR 97201 OHSU Center for SpatialSystems Biomedicine Computational Biology Program, Oregon Health and Science University, Portland, OR 97201;

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