Determining growth rates from bright-field images of budding cells through identifying overlaps.
Determining growth rates from bright-field images of budding cells through identifying overlaps.
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DOI：
10.7554/elife.79812
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发表时间：
2023-07-07
期刊：
影响因子：
7.7
通讯作者：
Swain PS
中科院分区：
文献类型：
--
作者：
Pietsch JMJ;Muñoz AF;Adjavon DA;Farquhar I;Clark IBN;Swain PS
许多生化调控最终控制着生长速度，特别是在微生物中。尽管延时显微镜可以显示细胞，但确定它们的生长速度是具有挑战性的，特别是对于那些不对称分裂的细胞，比如酿酒酵母，因为细胞经常在图像上重叠。在这里，我们介绍了萌芽酵母的出生注释器(BARE)，这是一种从无标记图像中确定单细胞生长率的算法。使用卷积神经网络，婴儿通过按大小分离细胞来解决重叠问题，并通过识别花蕾脖子将花蕾分配给母亲。Baby使用机器学习来跟踪细胞，确定血统，并以体积变化率来估计生长速度。使用Baby和微流控设备，我们发现芽的生长可能首先是由定时器控制的，然后是定时器控制的，核糖体生物发生的调节因子Sfp1的核浓度在生长速度发生变化之前发生变化，生长速度可以用于实时控制。通过估计单细胞生长率和健康状况，婴儿应该会产生更多的生物学洞察力。
Much of biochemical regulation ultimately controls growth rate, particularly in microbes. Although time-lapse microscopy visualises cells, determining their growth rates is challenging, particularly for those that divide asymmetrically, like Saccharomyces cerevisiae, because cells often overlap in images. Here, we present the Birth Annotator for Budding Yeast (BABY), an algorithm to determine single-cell growth rates from label-free images. Using a convolutional neural network, BABY resolves overlaps through separating cells by size and assigns buds to mothers by identifying bud necks. BABY uses machine learning to track cells and determine lineages and estimates growth rates as the rates of change of volumes. Using BABY and a microfluidic device, we show that bud growth is likely first sizer- then timer-controlled, that the nuclear concentration of Sfp1, a regulator of ribosome biogenesis, varies before the growth rate does, and that growth rate can be used for real-time control. By estimating single-cell growth rates and so fitness, BABY should generate much biological insight.