PO.CL12.04 · 临床研究
自体荧光和非特异性染色的定量建模可改善高度多重免疫荧光研究中的细胞表型分析和标志物评估
Quantitative modeling of autofluorescence and non-specific staining allows for improved cell phenotyping and marker assessment in highly multiplexed immunofluorescence studies
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
尽管免疫组织化学（IHC）在癌症诊断中具有基础性作用，但它存在若干重大局限，包括无法在单张切片上同时检测多个标志物，从而限制了真正的共表达分析。相比之下，序贯免疫荧光（seqIF）是一种多重蛋白生物标志物检测方法，它通过使用带有不同荧光基团的抗体，结合反复自动化的染色、成像和洗脱循环，实现在单张切片上同时检测多个标志物。虽然seqIF相较于IHC具有定量和可扩展性优势，但其使用荧光为下游计算分析带来了各种挑战，例如组织的自体荧光和非特异性荧光基团累积，这些问题会因多次染色、成像和洗脱循环而加重。Lunaphore COMET是一种seqIF系统，允许在方案开始时进行初始背景自体荧光采集。然而，我们观察到这一单次基线测量未能反映不同组织类型中各区域荧光在后续成像和洗脱循环中的变化，从而显著影响了包括细胞分型在内的下游分析。为进一步研究这些伪影，我们评估了成像循环期间多种组织类型上的自体荧光淬灭，以及所有通道（FITC、TRITC、Cy5和Cy7）中荧光基团的非特异性累积。我们发现背景自体荧光在部分（但非全部）循环中随序贯循环而降低，这导致下游标志物分析的信号强度人为降低。此外，我们发现非特异性荧光基团累积具有通道特异性、组织特异性和亚细胞定位特异性，进一步混淆了可解释性。总体而言，这些发现表明，利用单次基线自体荧光循环进行背景扣除是不充分的，可能导致显著的下游分析误差。虽然在每个洗脱循环后进行额外的成像循环并相应扣除背景可能缓解此问题，但由于时间、试剂用量和文件大小的增加，这并非总是可行或高效。我们发现，使用细胞水平的三次样条模型，共5个洗脱后成像循环即可准确预测20个循环的背景荧光（R2 = 0.94）。这些背景信号建模结果随后被用于计算各个循环在细胞水平上经背景校正的标志物表达值，从而改善了聚类分辨率和互操作性。总体而言，我们展示了seqIF中遇到的自体荧光问题，并提出了一种缓解这些问题以改善结果的新方法。
查看英文原文 English abstract
While fundamental in cancer diagnostics, immunohistochemistry (IHC) has several significant limitations, including the inability to simultaneously detect multiple markers per slide, which limits true co-expression analysis. In contrast, sequential immunofluorescence (seqIF), a multiplexed protein biomarker detection method, allows for simultaneous detection of multiple markers on a single slide by using antibodies with distinct fluorophores combined with the repeated automated cycles of staining, imaging, and elution. While seqIF has quantitative and scalability advantages over IHC, its use of fluorescence introduces various challenges for downstream computational analysis such as tissue's autofluorescence and non-specific fluorophore accumulation, which are compound by multiple cycles of staining, imaging and elution. The Lunaphore COMET, a seqIF system, allows for an initial background autofluorescence acquisition at the beginning of the protocol. However, we observed that this single baseline measurement did not account changes in the fluorescence of different regions in various tissue types with subsequent imaging and elution cycles, which significantly affected downstream analysis including cell typing.To further investigate these artifacts, we evaluated both autofluorescent quenching on a variety of tissue types during the imaging cycles as well as the non-specific accumulation of fluorophores in all channels (FITC, TRITC, Cy5, and Cy7). We found that the background autofluorescence decreased with sequential cycles in some, but not all cycles, which led to artificially decreased signal intensity for downstream marker analysis. Additionally, we found that non-specific fluorophore accumulation was channel specific, tissue specific, and subcellular localization specific, further confounding interpretability. Overall, these findings suggest that utilizing a single baseline autofluorescence cycle for background subtraction is insufficient and can lead to significant downstream analytical errors.While performing an additional imaging cycle after each elution cycle and subtracting background accordingly may remedy this problem, it is not always feasible or efficient due to increased time, reagent usage, and file size. We found that a total of 5 imaging cycles post-elution accurately predicted the background fluorescence for 20 cycles using a cell-level cubic spline model (R 2 = 0.94). These background signal modeling results are then utilized to calculate background-adjusted marker expression values at a cell level for individual cycles, which improved clustering resolution and interoperability. Overall, we demonstrate issues encountered with autofluorescence in seqIF and present a novel method for mitigating them to improve results.
利益披露 Disclosure
D. I. Mandel, None..
A. Colombo, None..
S. Mahov, None.