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Record W3169734859 · doi:10.21769/bioprotoc.4039

Quantitation of Secretory Granule Size in Drosophila Larval Salivary Glands

2021· article· en· W3169734859 on OpenAlexaff
J. Cheng-I, Julie A. Brill

Bibliographic record

VenueBIO-PROTOCOL · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsDrosophila melanogasterGranule (geology)SecretionBiologyCell biologySalivary glandSecretory proteinConfocalLarvaLive cell imagingConfocal microscopyAnatomyGeneBiochemistryCellEcology

Abstract

fetched live from OpenAlex

Maturation of secretory granules is a crucial process that ensures the bioactivity of cargo proteins undergoing regulated secretion. In Drosophila melanogaster, the larval salivary glands produce secretory granules that are up to four-fold larger in cross-sectional area after maturation. Therefore, we developed a live imaging microscopy approach to quantitate the size of secretory granules with a view to identifying genes involved in their maturation. Here, we describe the procedures of larval salivary gland dissection and sample preparation for live imaging with a fluorescence confocal microscope. Furthermore, we describe the workflow for measuring the size of secretory granules by cross-sectional surface area and statistical analysis. Our live imaging microscopy method provides a reliable read-out for the status of secretory granule maturation in Drosophila larval salivary glands., [摘要]分泌颗粒的成熟是一个关键过程,可确保货物蛋白质受调节分泌的生物活性。在果蝇中,幼虫唾液腺产生的分泌颗粒在成熟后的横截面积最多增加四倍。因此,我们制定了活体成像显微镜方法孔定量泰特分泌颗粒的大小,以期查明荷兰国际集团参与其成熟的基因。在这里,我们描述了用荧光共聚焦显微镜对幼虫唾液腺进行解剖的程序和用于实时成像的样品制备方法。此外,我们描述了通过横截面表面积和统计分析来测量分泌颗粒大小的工作流程。我们的实时成像显微镜方法为果蝇幼虫唾液腺分泌颗粒成熟的状态提供了可靠的读数。[背景]调节分泌是一个过程,在此过程中,诸如激素,消化酶和粘液等生物活性分子以协调的方式从专门的分泌细胞中分泌出来。因此,调节分泌对于维持动物体内的生理稳态至关重要。实例包括饭后释放胰岛素,响应病原微生物释放粘蛋白以及在体温升高时释放汗液。这些生物活性分子由内分泌或外分泌细胞产生,并储存在称为分泌颗粒的长效分泌细胞器中。 分泌颗粒的生物发生始于反高尔基网络,在那儿,分泌颗粒的货物聚集并萌芽为未成熟的分泌颗粒(Tooze,1991年和1998年; Borgonovo等人,2006年)。然后,未成熟的分泌性小颗粒会经历成熟过程,以完全发挥功能并具有分泌能力。成熟过程包括未成熟分泌颗粒的同型融合,去除不需要的物质以及加工货物(Tooze,1991; Arvan和Castle,1998)。分泌颗粒无法成熟会导致其货物的生物活性降低。例如,大多数激素以不活跃的激素形式进入未成熟的分泌颗粒。在成熟期间,分泌颗粒的内腔被酸化,并且激素原转化酶将激素原裂解为具有生物活性的激素(Moore等,2002)。因此,失效编颗粒成熟可具有生理后果,从而导致降低的活性或inefficie货物蛋白的核苷酸分泌。 尽管分泌颗粒成熟是调节分泌的关键步骤,但分泌颗粒成熟的测定并不简单。透射电子显微镜是测定分泌性颗粒成熟的标准方法之一。在电子显微照片中,分泌颗粒是电子致密的(Nitsch和Rinne,1981; Tatsuoka和Reese,1989)。分泌颗粒成熟的减少通常与电子密度或分泌颗粒数量的减少相关(Edwards等,2009; Cao等,2013; Du等,2016; Emperador-Melero等,2018; Hummer等人,2017; Rao等人,2020)。如果有抗体,可以在刺激后通过Western blotting和光密度法或ELISA测定血浆或细胞培养基中激素分泌的减少或激素原与激素的比率增加(Cao等人,2013; Du等人, 2016; Hummer等人,2017)。结合使用抗激素原抗体和分泌性颗粒标记的免疫荧光也可以揭示激素加工中的缺陷,如激素原信号强度的增强所表明的那样(Bogan等,2012; Cao等,2013)。在果蝇幼虫唾液腺是一个功能强大的基因为研究分泌颗粒的生物合成模型(Biyasheva等,2001;伯吉斯等人,2011和2012;托雷斯等人。2014; Csizmadia等人。2018年,马等。 ,2020; Neuman et al 。,2020)。进入第三龄幼虫阶段后24小时,幼虫唾液腺开始产生含胶蛋白的分泌颗粒。未成熟的分泌颗粒然后成熟在接下来的18小时,并响应于激素蜕皮激素的脉冲被分泌一次全部(Biyasheva等人,2001;伯吉斯等人。,2011) 。在变态过程中,分泌的胶蛋白将cases盒粘附在固体表面上。未成熟和成熟的分泌颗粒之间大小的差异可以是在横截面表面积(5微米2至4倍2 VS 。10-25微米2 )(马等人。,2020) 。因此,果蝇幼虫唾液腺是鉴定分泌性颗粒成熟所需的基因的优良系统。安德列斯实验室先前在其内源启动子的控制下产生了表达一种用GFP或DsRed标记的胶蛋白(Sgs3)的转基因亚麻(Biyasheva等,2001; Costantino等,2008)。使用这些品系,分泌颗粒可以通过共聚焦显微镜观察。我们的实验室已联合使用这些转基因株系与唾液腺-特定Gal4驱动子和UAS -控制RNAi的转基因株系,以确定所需要的分泌颗粒成熟的基因。在这里,我们提供了一个详细的协议,通过旋转盘共聚焦显微镜通过实时成像来可视化分泌颗粒。获取的数据进行分析与所述成像软件Volocity 6.3至QUANTI泰特分泌颗粒大小。分泌颗粒大小分布随后用作果蝇幼虫唾液腺中分泌颗粒成熟的读数。

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.053
GPT teacher head0.362
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

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Citations7
Published2021
Admission routes1
Has abstractyes

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