Decoding and recoding plant development

Abstract

The development of multicellular organisms has been studied for centuries, yet many critical events and mechanisms of regulation remain challenging to observe directly. Early research focused on detailed observational and comparative studies. Molecular biology has generated insights into regulatory mechanisms, but only for a limited number of species. Now, synthetic biology is bringing these two approaches together, and by adding the possibility of sculpting novel morphologies, opening another path to understanding biology. Here, we review a variety of recently invented techniques that use CRISPR/Cas9 and phage integrases to trace the differentiation of cells over various timescales, as well as to decode the molecular states of cells in high spatiotemporal resolution. Most of these tools have been implemented in animals. The time is ripe for plant biologists to adopt and expand these approaches. Here, we describe how these tools could be used to monitor development in diverse plant species, as well as how they could guide efforts to recode programs of interest.

An integrase toolbox to record gene-expression during plant development

“…The growing environmental pressures resulting from climate change make this adaptability increasingly important 1 . A better understanding of the mechanisms that underlie plant developmental plasticity will help guide the engineering of traits that can face current and future challenges 2 .…”

An integrase toolbox to record gene-expression during plant development

“…FIRST the directory where your files are and where your want to generate your graphs.# #Those need to be characters" # function to make boxplots of each set of tobacco injection data # reads in dataset to plot, figure size, construct order (order to plot from left to right) # and filename (path to which to save the generated plot) def plot_data(dataset, figuresize, construct_order, filename): fig, ax = plt.subplots(figsize=(figuresize, 8)) ax = sns.boxplot(x = 'Construct', y='Intensity Ratio', hue = 'Experiment', data = dataset, palette='Greys', width=0.7, order=construct_order, boxprops={'alpha':0.4}, showfliers=False) handles, labels = ax.get_legend_handles_labels() sns.stripplot(x = 'Construct', y='Intensity Ratio', hue = 'Experiment', data = dataset, jitter=True, palette='Greys', dodge = True, linewidth = 1, s=4, order=construct_order, ax=ax) ax.legend().set_visible(False) ax.figure.savefig(filename, bbox_inches='tight') # read in all raw platereader fluorescence measurement data (example in github repo) # replace with the path to your data, copy the line as many times as data files you have data1 = pd.read_excel(path_platereader_data, usecols="B:CS") # combine all platereader data outputs as one list # fill in list with variables above of platereader output dataframes # copy line for as many data files you have raw_data_list = [] # read in all 96 well plate data layout files # replace with your data layout file (example in github repo) # copy line for as many data files you have data_layout1 = pd.read_csv(path_layout_file) # combine all layouts into one list # fill in list with your layout dataframes layout_list = [] # calculate median values per well for entire data list med_per_exp = process_raw_data(raw_data_list) # merge all layouts into one df, then add calculated median intensity per well data as new column layout_merged_df = pd.DataFrame() for i in layout_list: layout_merged_df = pd.concat([layout_merged_df, i,], axis = "rows") layout_merged_df.insert(4, "YFP Intensity", med_per_exp[0]) layout_merged_df.insert(5, "RFP Intensity", med_per_exp [1]) layout_merged_df.insert(6, "Intensity Ratio", med_per_exp [2]) # drop any row with NA values (corresponding to unused wells) med_intensity_data = layout_merged_df.dropna() # changes float values for these constructs into string to be consistent with the rest of the data med_intensity_data.replace(439.0, '439', inplace=True) med_intensity_data.replace(440.0, '440', inplace=True) med_intensity_data.replace(472.0, '472', inplace=True) med_intensity_data.replace(473.0, '473', inplace=True)…”

“…FIRST the directory where your files are and where your want to generate your graphs.# #Those need to be characters" # function to make boxplots of each set of tobacco injection data # reads in dataset to plot, figure size, construct order (order to plot from left to right) # and filename (path to which to save the generated plot) def plot_data(dataset, figuresize, construct_order, filename): fig, ax = plt.subplots(figsize=(figuresize, 8)) ax = sns.boxplot(x = 'Construct', y='Intensity Ratio', hue = 'Experiment', data = dataset, palette='Greys', width=0.7, order=construct_order, boxprops={'alpha':0.4}, showfliers=False) handles, labels = ax.get_legend_handles_labels() sns.stripplot(x = 'Construct', y='Intensity Ratio', hue = 'Experiment', data = dataset, jitter=True, palette='Greys', dodge = True, linewidth = 1, s=4, order=construct_order, ax=ax) ax.legend().set_visible(False) ax.figure.savefig(filename, bbox_inches='tight') # read in all raw platereader fluorescence measurement data (example in github repo) # replace with the path to your data, copy the line as many times as data files you have data1 = pd.read_excel(path_platereader_data, usecols="B:CS") # combine all platereader data outputs as one list # fill in list with variables above of platereader output dataframes # copy line for as many data files you have raw_data_list = [] # read in all 96 well plate data layout files # replace with your data layout file (example in github repo) # copy line for as many data files you have data_layout1 = pd.read_csv(path_layout_file) # combine all layouts into one list # fill in list with your layout dataframes layout_list = [] # calculate median values per well for entire data list med_per_exp = process_raw_data(raw_data_list) # merge all layouts into one df, then add calculated median intensity per well data as new column layout_merged_df = pd.DataFrame() for i in layout_list: layout_merged_df = pd.concat([layout_merged_df, i,], axis = "rows") layout_merged_df.insert(4, "YFP Intensity", med_per_exp[0]) layout_merged_df.insert(5, "RFP Intensity", med_per_exp [1]) layout_merged_df.insert(6, "Intensity Ratio", med_per_exp [2]) # drop any row with NA values (corresponding to unused wells) med_intensity_data = layout_merged_df.dropna() # changes float values for these constructs into string to be consistent with the rest of the data med_intensity_data.replace(439.0, '439', inplace=True) med_intensity_data.replace(440.0, '440', inplace=True) med_intensity_data.replace(472.0, '472', inplace=True) med_intensity_data.replace(473.0, '473', inplace=True) # make an ordered list of constructs to iterate over construct_list = ['P1', 'P2', 'P4', 'P5', 'P16', 'P17', 'P18', 'P19', 'P22', 'P23', 'P24', 'P25', 'P27', 'P46', 'P47', 'P56', '439', '440', '472', '473', '294arf', '277'] # map construct name to index construct_dict = {'P1':0, 'P2':1, 'P4':2, 'P5':3, 'P16':4, 'P17':5, 'P18':6, 'P19':7, 'P22':8, 'P23':9, 'P24':10, 'P25':11, 'P27':12, 'P46':13, 'P47':14, 'P56':15, '439':16, '440':17, '472':18, '473':19, '294arf':20, '277':21} # makes a list of lists (ratio_vals) where each list is all the median ratiometric intensities for a construct # in order of the construct_list above ratio_vals = [] for i in construct_list: ratio_vals_temp = [] for ind, row in med_intensity_data.iterrows(): if row['Construct'] == i: ratio_vals_temp.append(row['Intensity Ratio']) ratio_vals.append(ratio_vals_temp) # example of running statistical analysis for group of pPP2AA3 constructs getStats(['P1', 'P2', 'P4', 'P5']) # subset pooled data into different dataframes for generating the separate plots main_tuning = med_intensity_data[med_intensity_data.Construct.isin(['P27', 'P46', 'P47', 'P56', '294', 277.0, '277'])==False] term_tuning = med_intensity_data[med_intensity_data.Construct.isin(['P1', 'P2', 'P46', 'P47', '294arf'])] ub_tuning = med_intensity_data[med_intensity_data.Construct.isin(['P23', 'P27', 'P56', '29...…”

“…However, detection is limited to a reduced amount of information at a time due to the limited number of fluorescent tags and to short timescales due to photobleaching and stress to the organisms. Recently, the development of synthetic, DNA-based recording systems have overcome some of the technical challenges of 'omic and microscopy techniques, allowing the sensing and relaying of multiple signals simultaneously during animal development (reviewed in 2 ).…”

“…The growing environmental pressures resulting from climate change make this adaptability increasingly important 1 . Better understanding of the mechanisms that underlie plant developmental plasticity will help guide the engineering of traits that can face current and future challenges 2 .…”