A collaboration between Parse Biosciences’ GigaLab and Vevo Therapeutics has generated what they say is the largest single-cell dataset to date. The Tahoe-100M dataset comprises 100 million cells and covers 60,000 conditions, 1,200 drug treatments, and 50 tumor models. Vevo plans to use the dataset to advance its artificial intelligence-based drug discovery efforts. .

Parse Biosciences的GigaLab和Vevo Therapeutics之间的合作产生了迄今为止最大的单细胞数据集。Tahoe-100M数据集包含1亿个细胞，涵盖60000种条件，1200种药物治疗和50种肿瘤模型。Vevo计划使用该数据集来推进其基于人工智能的药物发现工作。。

The partnership leveraged single-cell RNA sequencing capabilities from Parse as well as high-throughput sequencing functionality from Ultima Genomics. The project was completed in about a month.

该合作关系利用了Parse的单细胞RNA测序功能以及Ultima Genomics的高通量测序功能。该项目大约在一个月内完成。

According to Johnny Yu, PhD, Vevo’s CSO and co-founder, “The dataset is an important step forward for the Vevo team and the Mosaic platform.” Mosaic is designed to generate high-resolution in vivo data at scale. The company claims that its platform can measure how drugs impact cells from hundreds of patients generating millions of data points on changes in gene expression. .

Vevo的CSO和联合创始人Johnny Yu博士表示，“该数据集是Vevo团队和Mosaic平台向前迈出的重要一步。”Mosaic旨在大规模生成高分辨率的体内数据。该公司声称，其平台可以测量药物如何影响数百名患者的细胞，产生数百万个基因表达变化的数据点。。

The Tahoe-100 dataset contains entirely of data from perturbing diseased cells and “is 50x larger than all the public drug-perturbed single-cell data,” according to Vevo CEO and co-founder Nima Alidoust. Like many companies, the company is betting that AI-based tools can ingest this information and make interesting connections between drugs and disease pathways that open up new therapeutic opportunities.

Vevo首席执行官兼联合创始人尼玛·阿利杜斯特（NimaAlidoust）表示，Tahoe-100数据集完全包含来自扰动患病细胞的数据，“比所有公共药物扰动的单细胞数据大50倍”。像许多公司一样，该公司打赌基于人工智能的工具可以吸收这些信息，并在药物和疾病途径之间建立有趣的联系，从而开辟新的治疗机会。

A key challenge for drug developers is getting enough data to train models that are up to the task. .

药物开发人员面临的一个关键挑战是获得足够的数据来训练能够完成任务的模型。。

“The Tahoe-100M atlas entirely changes the game, allowing us to train much larger AI models that can better learn the language of the cell,” noted Hani Goodarzi, PhD, Vevo co-founder, associate professor at the University of California, San Francisco, and a core investigator at Arc Institute.

“Tahoe-100M atlas完全改变了游戏，使我们能够训练更大的人工智能模型，更好地学习细胞的语言，”Vevo联合创始人、加州大学旧金山分校副教授、Arc研究所核心研究员哈尼·古达齐博士指出。

Vevo plans to combine data from its single-cell atlas with AI models to search for novel targets and pathways for major cancer subtypes, as well as drug compounds that target these pathways. “Over the past two years, we’ve refined our platform and with access to the Parse GigaLab, we can now generate the data needed to power AI-based drug discovery at massive speed and scale,” Yu said. .

Vevo计划将其单细胞图谱的数据与AI模型相结合，以寻找主要癌症亚型的新靶点和途径，以及靶向这些途径的药物化合物。“在过去的两年中，我们已经完善了我们的平台，并且可以访问Parse GigaLab，我们现在可以生成所需的数据，以大规模的速度为基于AI的药物发现提供动力，”Yu说。。

Vevo plans to announce additional collaborations around the dataset in the first quarter of next year.

Vevo计划在明年第一季度宣布围绕该数据集的其他合作。

The partnership also helps demonstrate the benefits of Parse’s Evercode technology which powers GigaLab, specifically its ability to “deliver speed, quality, and immense scalability,” said Alex Rosenberg, Parse’s CEO and co-founder. GigaLab, an initiative that Parse launched earlier this year, targets researchers working on projects of 10 million single cells and larger for a range of applications.

Parse首席执行官兼联合创始人亚历克斯·罗森博格（AlexRosenberg）表示，该合作关系还有助于证明Parse的Evercode技术的优势，该技术为GigaLab提供了强大的动力，特别是它“提供速度、质量和巨大的可扩展性”的能力。Parse今年早些时候启动的一项倡议GigaLab的目标是从事1000万个单细胞和更大细胞项目的研究人员，用于一系列应用。

At the time of the launch, the company claimed a capacity of profiling 2.5 billion cells per year with plans to grow over time. .

在发布时，该公司声称每年分析25亿个电池的容量，并计划随着时间的推移而增长。。

Besides the Vevo partnership, Parse is exploring other large-scale projects that could leverage GigaLab’s single-cell sequencing capabilities including partnerships with biopharma companies as well as with large consortia.

除了Vevo合作伙伴关系之外，Parse正在探索其他可以利用GigaLab单细胞测序功能的大规模项目，包括与生物制药公司以及大型财团的合作伙伴关系。

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