Spatio-temporal distribution of environmental DNA derived from Japanese sea nettle jellyfish Chrysaora pacifica in Omura Bay, Kyushu, Japan

We surveyed the spatial and temporal distribution of Japanese sea nettle jellyfish Chrysaora pacifica in Omura Bay, Japan, using an environmental DNA (eDNA) method. In 2018, the C. pacifica eDNA concentration increased from March?May at all depths. The seasonal pattern of C. pacifica eDNA was consistent with previous reports based on visual observations along the Japanese coast. Thus, the eDNA method might have advantages to follow the seasonal pattern of C. pacifica while being less time-consuming and less laborious compared with traditional methods. The eDNA concentrations tended to reach a maximum near and/or below the pycnocline throughout this study. Therefore, the vertical distribution of C. pacifica medusae may have been restricted by strong pycnocline formation in July and August 2018. However, even with a weak pycnocline, which C. pacifica should be able to swim across, the apparent distribution of C. pacifica eDNA seems to be restricted by the pycnocline. Therefore, the eDNA method cannot, currently, accurately assess the absolute vertical distribution pattern of C. pacifica, especially when a pycnocline is formed

Data from: Inferring fitness landscapes and selection on phenotypic states from single-cell genealogical data

Recent advances in single-cell time-lapse microscopy have revealed non-genetic heterogeneity and temporal fluctuations of cellular phenotypes. While different phenotypic traits such as abundance of growth-related proteins in single cells may have differential effects on the reproductive success of cells, rigorous experimental quantification of this process has remained elusive due to the complexity of single cell physiology within the context of a proliferating population. We introduce and apply a practical empirical method to quantify the fitness landscapes of arbitrary phenotypic traits, using genealogical data in the form of population lineage trees which can include phenotypic data of various kinds. Our inference methodology for fitness landscapes determines how reproductivity is correlated to cellular phenotypes, and provides a natural generalization of bulk growth rate measures for single-cell histories. Using this technique, we quantify the strength of selection acting on different cellular phenotypic traits within populations, which allows us to determine whether a change in population growth is caused by individual cells' response, selection within a population, or by a mixture of these two processes. By applying these methods to single-cell time-lapse data of growing bacterial populations that express a resistance-conferring protein under antibiotic stress, we show how the distributions, fitness landscapes, and selection strength of single-cell phenotypes are affected by the drug. Our work provides a unified and practical framework for quantitative measurements of fitness landscapes and selection strength for any statistical quantities definable on lineages, and thus elucidates the adaptive significance of phenotypic states in time series data. The method is applicable in diverse fields, from single cell biology to stem cell differentiation and viral evolution

Plasmid sequences

The file contains the plasmid sequences of pLVK3, pLVSK4, pTmcherryK4, pTN001, and pTN002

Chronological and retrospective probabilities of single-cell lineages.

<p><b>A.</b> Chronological and retrospective probabilities on a fixed tree. Here we consider a representative fixed lineage tree spanning from time <i>t</i>₀ to <i>t</i>₁ = <i>t</i>₀ + <i>τ</i>. The number of cells in this tree at <i>t</i>₁ is cells, and each of these cells distinguishes a unique lineage (e.g. the cyan and orange lines in the tree). is the probability that a cell lineage <i>i</i> () is chosen by descending the tree from <i>t</i>₀ to <i>t</i>₁ (green arrow). At every division point, we randomly select one daughter cell’s lineage with the probability of 1/2 (light green arrows). The probability that we choose lineage <i>i</i> in this manner is , where <i>D</i>_<i>i</i> is the number of cell divisions on lineage <i>i</i>. is the probability of choosing cell lineage <i>i</i> among lineages with equal weight (pink arrow). Thus, . We call and the chronological probability and retrospective probability, respectively, based on the time directions of the green and pink arrows. The chronological and retrospective probabilities for the cell lineages 3 and 9 are shown in cyan and orange texts, respectively. <b>B.</b> A tree on which all the cell lineages have the same number of cell divisions. In this case, the chronological and the retrospective probabilities are equal for all the lineages. <b>C.</b> General case with a large collection of lineage trees. denotes a tree each descended from a different ancestor cell at time <i>t</i>₀. The definitions of the chronological and retrospective joint probabilities of division count <i>D</i> and lineage phenotype <i>x</i> are shown in green and pink, respectively. <i>n</i>(<i>D</i>, <i>x</i>) denotes the total number of cell lineages with <i>D</i> and <i>x</i>, i.e. .</p

Fitness landscapes and selection strength measured for <i>E. coli</i> F3NW.

<p><b>A.</b> Population growth curves. Green curve is for −Sm condition, and red for +Sm condition (the color correspondence is the same for all the following panels). Relative population size on the y-axis is the number of cells at each time point normalized by the number of cells at <i>t</i> = 0 min. The error bars in all panels are the standard deviations of three independent experiments. Growth rate difference became apparent only after <i>t</i> = 100 min. Hence, we set <i>t</i>₀ = 100 min and <i>t</i>₁ = 300 min in the following analyses. The numbers of cells at time <i>t</i>₀ and <i>t</i>₁ are shown in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006653#pgen.1006653.s003" target="_blank">S2 Table</a>. <b>B-D.</b> Comparison of population growth rate Λ (B), chronological mean lineage fitness (C), and selection strength for division count <i>S</i>[<i>D</i>] (D), between −Sm and +Sm conditions. <i>p</i>-values by <i>t</i>-test are 0.036, 0.077, and 0.34, respectively (<i>n</i> = 4). <b>E-G.</b> Fitness landscapes <i>h</i>(<i>x</i>) (upper panels) and chronological distributions <i>P</i>^cl(<i>x</i>) (lower panels) for elongation rate (E), protein production rate (F), and protein concentration (G). <b>H.</b> Selection strengths. We compared selection strengths , , and between −Sm and +Sm conditions, finding no statistically significant differences for all the phenotypes. The <i>p</i>-values are 0.12 for , 0.25 for , and 0.48 for , respectively (<i>n</i> = 4). <b>I.</b> Relative selection strengths. Again, no statistically significant differences were found. The <i>p</i>-values are 0.057 for , 0.27 for , and 0.69 for , respectively (<i>n</i> = 4). <b>J.</b> Relationship between relative selection strength and squared correlation coefficient between and <i>h</i>(<i>x</i>), where , , or . The correlation coefficients were evaluated by both chronological and retrospective probabilities.</p

Fitness landscapes and selection strength measured for <i>E. coli</i> F3/pTN001.

<p><b>A.</b> Population growth curves. Green curve is for −Sm condition, and red for +Sm condition (the color correspondence is the same for all subsequent panels). Relative population size on the y-axis is the number of cells at each time point normalized by the number of cells at <i>t</i> = 0 min. The error bars in all panels are the standard deviations of three independent experiments. Growth rate difference became apparent only after <i>t</i> = 200 min. Hence, we set <i>t</i>₀ = 200 min and <i>t</i>₁ = 400 min in the following analyses. The results with <i>t</i>₀ = 0 min and <i>t</i>₁ = 200 min are shown in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006653#pgen.1006653.s010" target="_blank">S6 Fig</a>. The numbers of cells at times <i>t</i>₀ and <i>t</i>₁, which specify the number of cell lineages used in the analysis, are given in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006653#pgen.1006653.s002" target="_blank">S1 Table</a>. <b>B-D.</b> Comparison of population growth rate Λ (B), chronological mean lineage fitness (C), and selection strength for division count <i>S</i>[<i>D</i>] (D), between −Sm and +Sm conditions. <i>p</i>-values by <i>t</i>-test are 0.013, 0.010, and 0.529, respectively (<i>n</i> = 3). <b>E-G.</b> Fitness landscapes <i>h</i>(<i>x</i>) (upper panels) and chronological distributions <i>P</i>^cl(<i>x</i>) (lower panels) for elongation rate (E), protein production rate (F), and protein concentration (G). The fitness landscapes for elongation rate and protein production rate were barely distinguishable between −Sm and +Sm conditions, whereas that for protein concentration shows a slight downshift in +Sm condition. In contrast, shift of chronological distributions was observed for elongation rate and protein production rate, but not for protein concentration. <b>H.</b> Selection strengths. We compared selection strengths , , and between −Sm and +Sm conditions, finding a statistically significant difference only for (<i>p</i> < 0.05). The <i>p</i>-values are 0.34 for , 0.044 for , and 0.58 for , respectively (<i>n</i> = 3). <b>I.</b> Relative selection strengths. Again, the difference is statistically significant only for (<i>p</i> < 0.05). The <i>p</i>-values are 0.21 for , 0.024 for , and 0.14 for , respectively (<i>n</i> = 3). <b>J.</b> Relationship between relative selection strength and squared correlation coefficient between and <i>h</i>(<i>x</i>), where , , or . The correlation coefficients were evaluated by both chronological and retrospective probabilities.</p

Schematic pictures that show the relations among chronological and retrospective phenotype distributions, fitness landscape, and selection strength.

<p><b>A.</b> Weak selection. When the chronological (green) and retrospective (pink) probability distributions are similar, the fitness landscape <i>h</i>(<i>x</i>) largely does not change over the range of phenotypic values <i>x</i>, and the selection strength <i>S</i>[<i>x</i>] is approximately 0. <b>B.</b> Strong selection. When the retrospective probability distribution significantly deviates from the chronological distribution, the fitness landscape <i>h</i>(<i>x</i>) changes greatly over the range of phenotypic values <i>x</i>. In this case, the selection strength <i>S</i>[<i>x</i>] is greater than zero.</p

An example of cell lineage data obtained by single-cell time-lapse measurement.

<p><b>A</b> Time-lapse images of a growing microcolony of <i>E. coli</i> F3/pTN001. This strain expresses a fluorescent protein, Venus-YFP, from a low copy plasmid (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006653#sec013" target="_blank">Materials and Methods</a>). One can obtain the time-lapse images for many microcolonies (ca. 100) starting from different ancestral cells in a single experiment. Scale bars, 5 μm. <b>B</b> Cell lineage tree for the microcolony in (A), which shows the genealogical relationship of individual cells. <b>C</b> Transitions of cell sizes. Different lines correspond to different single-cell lineages on the tree (B). Due to the fluctuation of growth parameters such as elongation rate and division interval, the transition patterns are variable among the cell lineages. <b>D</b> Transitions of mean fluorescence intensity along single-cell lineages on the tree (B). Again, the transition patterns are variable among the cell lineages.</p

Contributions of individual cells’ response Δ〈<i>h</i>(<i>x</i>)〉^cl and change of selection strength Δ<i>S</i>[<i>x</i>] to fitness gain 〈<i>h</i>(<i>x</i>)〉^rs for the environmental change from −Sm to +Sm.

<p>Contributions of individual cells’ response Δ〈<i>h</i>(<i>x</i>)〉^cl and change of selection strength Δ<i>S</i>[<i>x</i>] to fitness gain 〈<i>h</i>(<i>x</i>)〉^rs for the environmental change from −Sm to +Sm.</p