SBIR Phase I: Hardware and Software Systems for High Throughput, High Cell Density Fermentation

SBIR第一阶段项目旨在开发一种高通量、基于图像的单细胞分析系统。 该系统将用于表征生物研究期间捕获的高细胞密度多微生物样品。 最初，该技术将应用于量化生物样品中极低水平的微生物污染的问题。 微生物污染的定量和控制是工业发酵和生物制剂生产中的重要挑战。 不存在用于快速和灵敏地检测微生物污染物的方法。 传统的污染检测方法涉及耗时的细胞培养或用显微镜进行手动和不精确的检查。 拟议的技术将提供自动化和快速的样品表征，使分析速度比目前的系统快1000倍。 这种增加的吞吐量将另外允许量化较低水平的污染。 虽然最初应用于研究，但该技术也可用作医学领域的分析和诊断工具。 例如，该分析系统可用于搜索人类样本中可能由于镰状细胞贫血等疾病而出现的畸形红细胞，或用于确定免疫细胞的相对丰度和形态，该项目将创建一个硬件平台和一个并行计算机视觉系统，该系统能够完全表征1mL中的每个细胞。五分钟内取出生物样本 为了分析从具有高细胞密度的发酵过程中采集的样品，需要显微照相系统能够在分析窗口中处理超过十亿个细胞。 该项目旨在开发四个子系统：用于分离和隔离细胞的高压微流体系统，成像系统，一套用于对细胞图像数据进行分类的高通量算法，以及一个水平可扩展的计算平台，在该平台上运行分类代码。 该项目将开始开发一个低吞吐量，功能齐全的原型。 然后，工作重点将放在并行开发四个主要子系统上，使用在原型设计工作中获得的知识来指导以后的工作。 其他阶段将侧重于集成高吞吐量版本的子系统。 与传统的细胞仪系统相比，完全实现的单细胞显微成像系统将在速度和特异性方面实现飞跃。 在没有人工干预的情况下，研究人员将能够以开放式、无标签的方式对生物样品进行十亿分之一级的检测。

This SBIR Phase I project proposes to develop a high throughput, imagery-based single cell analysis system. The system will be used to characterize high cell density polymicrobial samples captured during biological research. Initially, the technology will be applied to the problem of quantifying extremely low levels of microbial contamination in biological samples. The quantification and control of microbial contamination is an important challenge in industrial fermentation and in the manufacturing of biologics. Methods for rapid and sensitive detection of microbial contaminants do not exist. Traditional contamination detection methods involve time-consuming culturing of cells or manual and imprecise checks with a microscope. The proposed technology will provide automated and rapid sample characterization, allowing a speed of analysis that is 1000 times faster than current systems. This increased throughput will additionally allow lower levels of contamination to be quantified. While initially applied to research, the technology can also be used as an analytical and diagnostic tool in the medical field. For example, this analysis system could be used to search for malformed red blood cells in a human sample that might arise due to a disease like sickle cell anemia, or to determine the relative abundance and morphology of immune cells, an analysis that would help identify certain leukemias.This project will create a hardware platform and a parallelized computer vision system that is capable of fully characterizing each cell in a 1mL biological sample in under five minutes. To analyze samples taken from a fermentation process with high cell densities requires the micrography system to be capable of processing over one billion cells in the analysis window. The project aims to develop four subsystems: a high pressure microfluidic system for separating and isolating cells, an imaging system, a set of high throughput algorithms to classify the cellular imagery data and a horizontally-scalable computing platform on which to run the classifying code. The project will begin by developing a low throughput, fully functioning prototype. The efforts will then focus on developing the four primary subsystems in parallel, using knowledge gained in the prototyping efforts to guide the later work. Additional phases will focus on the integration of high throughput versions of the subsystems. The fully realized single cell micrography system will represent a leap forward in speed and specificity compared to traditional cytometry systems. With no manual intervention and in an open-ended, label-free manner, researchers will be able to conduct parts per billion-level inspection of a biological sample.