Small-scale Subcellular Fractionation with Sucrose Step Gradient
Bibliographic record
Abstract
Here, we introduce the protocol for small-scale and simple subcellular fractionation used in our recent publication (Taguchi et al., 2013), which uses homogenization by passing through needles and sucrose step-gradient. Subcellular fractionation is a very useful technique but usually a large number of cells are required. Because we needed subcellular fractionation of transiently-transfected cells, we developed a protocol for smaller numbers of cells. Our protocol for the subcellular fractionation is based on the protocol published by de Araújo and Huber (de Araujo et al., 2007), although substantial modifications have been made according to our experiences and information from personal communications. As optimal conditions seem to vary between cell lines, we advise to further modify the protocol to optimize for individual experiments. Our method is simple but sufficient for analysis of integral membrane proteins or proteins anchored to organelles by glycosylphosphatidylinositol or other lipid anchors, e.g. prion protein. However, proteins non-covalently attached to membranes or membrane proteins of organelles seem to be more prone to dissociation from the organelles during preparation and, if these proteins are the object of study, further modifications might be necessary. Unlike in a continuous gradient, where a protein of interest is scattered over a wide range, step-gradient fractionation is advantageous in detection of relatively small amounts of proteins from small-scale experiments, because it concentrates the protein of interest in one fraction, if an appropriate combination of sucrose concentrations is used., 在这里，我们介绍了在我们最近的出版物（Taguchi等人，2013）中使用的用于小规模和简单的亚细胞分离的方案，其通过穿过针和蔗糖梯度梯度使用匀浆。 亚细胞分离是一种非常有用的技术，但通常需要大量的细胞。因为我们需要瞬时转染细胞的亚细胞分离，我们开发了用于较小数量细胞的方案。我们的用于亚细胞分级的方案基于deAraújo和Huber（de Araujo等人，2007）公布的方案，尽管根据我们的经验和来自个人通信的信息进行了实质性的修改。由于最佳条件似乎在细胞系之间不同，我们建议进一步修改方案以优化个别实验。我们的方法很简单，但足以分析通过糖基磷脂酰肌醇或其他脂质锚例如朊病毒蛋白锚定到细胞器的内在膜蛋白或蛋白质。然而，非共价连接到膜或细胞器的膜蛋白的蛋白质似乎更容易在制备过程中从细胞器中解离，并且如果这些蛋白质是研究的目的，则可能需要进一步的修饰。 不同于连续梯度，其中感兴趣的蛋白质分散在宽范围内，步梯度分级分离在小规模实验中检测相对少量的蛋白质是有利的，因为它将感兴趣的蛋白质浓缩如果使用蔗糖浓度的适当组合。
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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".