Technology Innovation Lab, New York Genome Center, New York, NY, USA.
Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA.
Sci Rep. 2022 Mar 24;12(1):5081. doi: 10.1038/s41598-022-08740-w.
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, comprising an epifluorescence microscope with integrated fluidics, into an open platform for programmable applications without need for specialized engineering or software development expertise. 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. Given the widespread availability of HiSeq 2500 platforms and the simplicity of the modifications required to repurpose these systems, PySeq2500 enables non-specialists to develop and implement state of the art fluidics coupled imaging methods in a widely available benchtop system.
荧光显微镜是生命科学中的一种关键方法。最先进的组学方法将荧光显微镜与复杂的方案相结合，在每个样本的数百万像素中可视化数十到数千个特征。这些组学方法需要在数天或数周的迭代化学和成像循环中精确控制温度、试剂应用和图像采集参数。这些方法的自动化执行可以实现稳健且可重复的数据生成。然而，用于温度控制、流体耦合荧光成像的商业解决方案很少，并且定制仪器的实施需要专门的工程专业知识。在这里，我们展示了 PySeq2500，这是一个开源的 Python 代码库和流控池设计，它将 Illumina HiSeq 2500 仪器（包含集成流体学的荧光显微镜）转换为一个开放平台，用于可编程应用，而无需专门的工程或软件开发专业知识。可定制的 PySeq2500 协议可实现涉及同时进行 4 通道图像采集、温度控制、试剂交换、稳定定位和样本完整性的实验设计，实验时间可延长。为了演示复杂的、多天的工作流程的可访问自动化，我们使用 PySeq2500 系统来无人值守地执行迭代间接免疫荧光成像 (4i)。我们的自动化 4i 方法使用市售的抗体，经过多个染色、成像和抗体洗脱循环，可在小鼠和死后人类脊髓切片中构建高度多重化的细胞类型和病理特征图谱。鉴于 HiSeq 2500 平台的广泛可用性以及重新利用这些系统所需的修改的简单性，PySeq2500 使非专业人员能够在广泛可用的台式系统中开发和实施最先进的流体耦合成像方法。
