6 emerging trends in life sciences

The life sciences labs of the future will require skills in automation, open source technologies, artificial intelligence, and many other emerging areas. Here's what you need to know. In the early 21st century, wet laboratory skills in molecular biology, cell biology, and proteomics are key for anyone seeking a life sciences career. While expertise in these areas remains essential, future laboratories will require different skill sets based on emerging trends in research and technology.

Here are six skill areas we think it’s helpful to familiarize yourself with to advance your career and prepare you for the life sciences lab of the future.

1. 自动化知识
   2018 年世界经济论坛上发表的研究发现，到 2025 年，自动化设备预计将执行当今一半以上的任务。机器人还将在相当的时间内创造约 6000 万个新工作岗位。到 2026 年，仅液体处理机器人市场规模就预计将超过 70 亿美元，很难想象在不久的将来，自动化技术不会为你的简历增色不少。

Automation offers significant advantages to life science researchers, including improved data quality, increased cost efficiency, scalability, and more time to do other things in the lab other than repeat experiments. Automation also benefits new complex biotechnologies such as next-generation sequencing or mass spectrometry proteomic analysis, which require increasingly complex workflows that are sometimes too complex and time-consuming for humans to successfully execute. Automated innovation and capabilities are proliferating as the need for speed and throughput increases in drug discovery, diagnostics and even basic research.

Given the huge potential for fully automated workflows, many research labs are taking scientists away from lab work—pulling them off their benches and putting them in front of their computers to design experiments for lab robots to complete instead. Execute it yourself. This more effectively leverages scientists' understanding of biology, leaving repetitive lab work to robots so biologists can solve difficult scientific questions.

Ginkgo Bioworks designs custom organisms for customers and builds its own factories, leveraging software and hardware automation to scale the process. Synthego is the first and only company to offer full-stack genome engineering solutions, achieving this by leveraging cloud-based software automation. Synthace utilizes both hardware and software to aid large-scale automated laboratories. Of course, hundreds of biologists around the world use Opentrons software and hardware to automate protocols and workflows for a variety of experiments, from basic dilutions to PCR prep and NGS. Exploring how these companies are using automation will help you understand how automation is changing life science research.

2. Open source, collaboration and shareability skills

Open source refers to software whose source code is free for anyone to view, use, modify, and share. It allows users to build and learn from existing code, while promoting and encouraging collaboration and innovation among users around the world.

Open source applications in the life sciences are used to ingest the vast data sets produced by genomics and other related applications, such as the Ensembl Genome Browser database, which makes genomic and other related data accessible to any interested user. Open source computing technology is also used to model and simulate organisms: OpenWorm uses it to create virtual nematodes, while Virtual Cell uses it to model and simulate cells, and epidemiological researchers share genomic data to speed up pathogen analysis and identify the source of outbreaks. .

As the use of open source in life science disciplines continues to evolve, practical expertise in coding and data sharing methods will become a major asset for life scientists – so becoming familiar with some of these tools now will give you an advantage in the future. A good place to start is with open source protocol sharing platforms, such as Protocols.io and the Opentrons protocol library, which allow scientists to discover and co-develop protocols, and Plasmotron.org, which allows users to build on open source code.

3. 科学传播
   虽然所有科学家都通过科学演讲、研究论文、文献综述和参加会议来相互交流他们的工作，但他们需要使用不同的方法与公众交流。

The ability to effectively communicate the applications and impact of life science research to non-technical audiences creates important connections between scientists and society. Effective communication in this area involves using non-technical, plain language, making concepts easy to understand, and learning how to reach and engage the public. Practicing both disciplines encourages important discussion and debate and makes knowledge about scientific developments transparent and accessible to everyone. You can join thousands of biologists working to improve science communication and hone your science communication skills by participating in voluntary science outreach events, sharing your research activities on social media, or creating your own science blog or podcast.

4. 机器学习和人工智能
   虽然听起来很有未来感，但我们每天都被机器学习辅助的人工智能应用所包围：智能手机上的语音助手、网站上的实时聊天功能、社交媒体推送中的定向广告等等。这些应用利用复杂的算法和海量数据集来训练计算机像人类一样工作和反应。这个过程改进了计算机的学习过程，使它们在响应条件和产生结果方面更加高效——迅速提高了未来发现的速度。

In the life sciences, machine learning has revolutionized the speed of research and diagnosis through its ability to distinguish cells, analyze genomic data, perform image analysis and detect indicators of disease earlier and more sensitively than previous methods. One major growth area is using machine learning to design experiments. Asimov uses open source data to develop machine learning algorithms that connect large-scale data sets with biological mechanistic models to build biological circuit experiments. Cello uses machine learning to automatically design biological circuits in living cells. Another major growth area is bioinformatics. Deep Genomics uses machine learning to collect, analyze and process genomic data to develop better, more targeted medicines. Some companies, like Atomwise, are even using deep learning frameworks to try to screen drug candidates in software. Familiarity with these emerging applications will help you understand the future uses of this technology.

5. 使用 CRISPR 进行基因编辑
   2000 年代中期发现的 CRISPR 是生命科学的一个转折点，因为它可用于基因工程。虽然我们现在都熟悉 CRISPR 平台，但基因编辑平台的应用影响深远——并且经常与许多其他新兴领域重叠。Oxford Genetics 提供用于基因编辑的实验设计工具，帮助简化工作流程。Synthego 再次利用机器学习来推动基因工程中的实验设计。 CRISPR Therapeutics 利用 CRISPR 基因编辑平台开发针对血液疾病（如镰状细胞性贫血）的药物，而 Caribou Biosciences、Editas Medicine 和 Cellectis 则利用 CRISPR 和其他基因编辑技术（如 TALEN）来修改 T 细胞以针对癌细胞。

With the global CRISPR market expected to grow six-fold to $3 billion by 2023, opportunities to leverage gene-editing tools in future life science labs should be numerous.

6. 单细胞技术
   快速发展的技术允许将蛋白质组学、基因组学、转录组学和表观遗传学技术应用于单细胞，为控制发育、基因表达、组织异质性和疾病机制的复杂生物过程提供了新颖而关键的见解。这些技术对于分析循环肿瘤细胞和稀有干细胞等生物现象特别有用，而这些现象对于标准“组学”应用来说具有挑战性，甚至是不可能的。基因组编辑、自动化和微流控技术的同步发展进一步促进了单细胞应用中常见的较小样本的快速高通量分析。10x Genomics 使用单细胞癌症基因组学检测来分析癌细胞。Metafluidics 是一个用于复制或重新混合微流控设备的开源设计和协议文件数据库，是该领域的一个很好的信息存储库。

Growth in the single-cell analysis field is driven by basic research and growing demand for early disease detection technologies, prenatal screening, biomarker discovery, liquid biopsies, and biopharmaceutical development.