[1]:

import emzed
import os

# path to files
data_folder = os.path.join(os.getcwd(), "tutorial_data")
path = os.path.join(data_folder, "CoA_ester_ms_ms2.mzXML")
coa = emzed.io.load_peak_map(path)

start remote ip in /root/.emzed3/pyopenms_venv

pyopenms client started.
connected to pyopenms client.

7 Targeted sample analysis¶

In Chapter 2 we started targeted extraction of amino acids with a table containing the corresponding m/z values of protonized monoisotopic masses. The emzed module ``targeted`` provides the method ``solution_space`` supporting a more advanced approach of targeted peak extraction that includes possible isotopologues and adducts: ~~~ solution_space(targets, adducts, explained_abundance=0.999, resolution=None) ~~~

Arguments:

targets: a table with the 2 mandatory columns id and mf, with column mf containing the molecular formulas of interest
adducts: a table of the emzed module emzed.adducts with selected adducts
explained_abundance: determines the number of isotopologue peaks taken into account
resolution: the mass resolution of the MS instrument $\frac{m z}{F W H M}$

The function returns the mz solution space of molecular formulas of selected adducts including isotopologues.

The number of included isotopologues depends on the parameter explained_abundance. Note, the larger a molecule, the more isotopologues will be included with the same value. As an example, we build the m/z solution space of the acetyl-CoA M+H ion:

[2]:

# 1. we create the target table
target = emzed.to_table("mf", ["C23H38N7O17P3S"], str)
target.add_enumeration()
# 2. we select the adduct of interest
adducts = emzed.adducts.M_plus_H
# 3. we build the table
sp = emzed.targeted.solution_space
t = sp(target, adducts, explained_abundance=0.95, resolution=6e4)
t

[2]:

id	target_id	mf	adduct_id	adduct_name	m_multiplier	adduct_add	adduct_sub	z	sign_z	full_mf	isotope_id	isotope_decomposition	m0	abundance	mz
int	int	str	int	str	int	str	str	int	int	str	int	str	MzType	float	MzType
0	0	C23H38N7O17P3S	20	M+H	1	H		1	1	C23H39N7O17P3S	0	-	810.133604	0.745	810.133056
1	0	C23H38N7O17P3S	20	M+H	1	H		1	1	C23H39N7O17P3S	1	-	811.136597	0.196	811.136049
2	0	C23H38N7O17P3S	20	M+H	1	H		1	1	C23H39N7O17P3S	2	-	812.136114	0.059	812.135565

Once we determined the solution space, we can continue as explained in chapter 2:

[3]:

# we initially use the whole RT range of the peakmap

rtmin, rtmax = coa.rt_range()
mztol = 0.003


def add_peak_columns(t, rtmin, rtmax, peakmap):
    t.add_column("mzmin", t.mz - mztol, emzed.MzType)
    t.add_column("mzmax", t.mz + mztol, emzed.MzType)
    t.add_column_with_constant_value("rtmin", rtmin, emzed.RtType)
    t.add_column_with_constant_value("rtmax", rtmax, emzed.RtType)
    t.add_column_with_constant_value(
        "peakmap", peakmap, emzed.PeakMap
    )


add_peak_columns(t, rtmin, rtmax, coa)

[4]:

t = emzed.quantification.integrate(
    t, "linear", ms_level=1, show_progress=False
)
t

[4]:

id	target_id	mf	adduct_id	adduct_name	m_multiplier	adduct_add	adduct_sub	z	sign_z	full_mf	isotope_id	isotope_decomposition	m0	abundance	mz	mzmin	mzmax	rtmin	rtmax	peak_shape_model	area	rmse	valid_model
int	int	str	int	str	int	str	str	int	int	str	int	str	MzType	float	MzType	MzType	MzType	RtType	RtType	str	float	float	bool
0	0	C23H38N7O17P3S	20	M+H	1	H		1	1	C23H39N7O17P3S	0	-	810.133604	0.745	810.133056	810.130056	810.136056	5.10 m	32.01 m	linear	2.67e+07	0.00e+00	True
1	0	C23H38N7O17P3S	20	M+H	1	H		1	1	C23H39N7O17P3S	1	-	811.136597	0.196	811.136049	811.133049	811.139049	5.10 m	32.01 m	linear	7.38e+06	0.00e+00	True
2	0	C23H38N7O17P3S	20	M+H	1	H		1	1	C23H39N7O17P3S	2	-	812.136114	0.059	812.135565	812.132565	812.138565	5.10 m	32.01 m	linear	2.10e+06	0.00e+00	True

The resulting a peaks table containing the isotopologues M0, M1, and M2 of M+H ion.

The method ``solution_space`` is also extremely useful to extract and group all LC-MS peaks originating from a known compound. In our example, we know acetyl-CoA forms the major ion M+H eluting between rtmin = 890s and rtmax = 920s. We will now search the m/z solution space of acetyl-CoA for all other possible peaks originating from the compound in the positive ESI mode.

[5]:

# use the adduct table with all common positive adducts
adducts = emzed.adducts.positive
# build the solution space
t = sp(target, adducts, explained_abundance=0.95, resolution=6e4)
add_peak_columns(t, 890, 920, coa)
# extract peaks
t = emzed.quantification.integrate(t, "linear", show_progress=False)
t = t.filter(t.area > 1e3)  # keep only features with significant area
print("number of peaks:", len(t))

number of peaks: 16

In given example, we can find 16 peaks origination from the single compound acetyl-CoA.