Single Cell Analysis and Sorting of Aspergillus fumigatus by Flow Cytometry
Bibliographic record
Abstract
Experimental results in fungal biology research are usually obtained as average measurements across whole populations of cells, whilst ignoring what is happening at the single cell level. Microscopy has allowed us to study single-cell behavior, but it has low throughput and cannot be used to select individual cells for downstream experiments. Here we present a method that allows for the analysis and selection of single fungal cells in high throughput by flow cytometry and fluorescence activated cell sorting (FACS), respectively. This protocol can be adapted for every fungal species that produces cells of up to 70 microns in diameter. After initial setting of the flow cytometry gates, which takes a single day, accurate single cell analysis and sorting can be performed. This method yields a throughput of thousands of cells per second. Selected cells can be subjected to downstream experiments to study single-cell behavior., [摘要]真菌生物学研究的实验结果通常作为对整个细胞群体的平均测量而获得，而忽略了单个细胞水平上发生的事情。显微镜让我们来研究单-细胞的行为，但它具有低吞吐量，并且不能被用来选择单个细胞下游实验。在这里，我们提出一种方法，可以通过流式细胞术和荧光激活细胞分选（FACS）分别以高通量分析和选择单个真菌细胞。这个协议可以适于产生最多的细胞至70个微米每真菌物种小号在直径上。在流式细胞仪门的初始设置（需要一天的时间）之后，可以执行准确的单细胞分析和分选。此方法每秒产生数千个单元的吞吐量。所选单元格可进行下游实验来研究单-细胞的行为。[背景]真菌生物学的研究经常被DEPE ň凹痕对细胞总人口的平均测量，从而错过了什么是在单个细胞水平上发生的事情。对于丝状真菌来说，情况尤其如此，因为菌丝会通过根尖的延伸而生长，并在根尖下分支，并可能与邻近的菌丝融合。在菌丝的相互连接，缠绕的质量这种行为的结果，殖民地，这使得它具有挑战性的研究单-细胞的行为。因此，在单电池的最研究具有焦点版上非重叠菌丝正在使用显微镜位于菌落余量（Vinck等人，2005。和2011; Bleichrodt等人。，2012和2015）。由于菌丝附着在菌落上，因此使用图像分析对菌丝进行分割具有挑战性。在这里，我们开发了一种流式细胞仪协议来分析和选择的单细胞通过高p UT。该协议适用于例如分析孢子的萌发；荧光转化子的选择; 米的最小值：我nhibitory Ç oncentration （MIC）试验。为单细胞组学方法获得细胞；鉴定细胞的异质亚群；细胞聚集试验，可能与研究发酵罐沉淀形成的初始阶段有关；以及分析和选择任何小于70微米的荧光标记细胞靶标（无论是染色的还是基因编码的）（Bleichrodt and Read，2019）。
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".